references.bib

@misc{180103924UnreasonableEffectiveness,
  title = {[1801.03924] {{The Unreasonable Effectiveness}} of {{Deep Features}} as a {{Perceptual Metric}}},
  urldate = {2024-09-28},
  howpublished = {https://arxiv.org/abs/1801.03924},
  file = {/Users/zain/Zotero/storage/G6KB3YGD/1801.html}
}

@article{ackermannOperationFreeelectronLaser2007,
  title = {Operation of a Free-Electron Laser from the Extreme Ultraviolet to the Water Window},
  author = {Ackermann, W. and Asova, G. and Ayvazyan, V. and Azima, A. and Baboi, N. and B{\"a}hr, J. and Balandin, V. and Beutner, B. and Brandt, A. and Bolzmann, A. and Brinkmann, R. and Brovko, O. I. and Castellano, M. and Castro, P. and Catani, L. and Chiadroni, E. and Choroba, S. and Cianchi, A. and Costello, J. T. and Cubaynes, D. and Dardis, J. and Decking, W. and {Delsim-Hashemi}, H. and Delserieys, A. and Di Pirro, G. and Dohlus, M. and D{\"u}sterer, S. and Eckhardt, A. and Edwards, H. T. and Faatz, B. and Feldhaus, J. and Fl{\"o}ttmann, K. and Frisch, J. and Fr{\"o}hlich, L. and Garvey, T. and Gensch, U. and Gerth, Ch and G{\"o}rler, M. and Golubeva, N. and Grabosch, H.-J. and Grecki, M. and Grimm, O. and Hacker, K. and Hahn, U. and Han, J. H. and Honkavaara, K. and Hott, T. and H{\"u}ning, M. and Ivanisenko, Y. and Jaeschke, E. and Jalmuzna, W. and Jezynski, T. and Kammering, R. and Katalev, V. and Kavanagh, K. and Kennedy, E. T. and Khodyachykh, S. and Klose, K. and Kocharyan, V. and K{\"o}rfer, M. and Kollewe, M. and Koprek, W. and Korepanov, S. and Kostin, D. and Krassilnikov, M. and Kube, G. and Kuhlmann, M. and Lewis, C. L. S. and Lilje, L. and Limberg, T. and Lipka, D. and L{\"o}hl, F. and Luna, H. and Luong, M. and Martins, M. and Meyer, M. and Michelato, P. and Miltchev, V. and M{\"o}ller, W. D. and Monaco, L. and M{\"u}ller, W. F. O. and Napieralski, O. and Napoly, O. and Nicolosi, P. and N{\"o}lle, D. and Nu{\~n}ez, T. and Oppelt, A. and Pagani, C. and Paparella, R. and Pchalek, N. and {Pedregosa-Gutierrez}, J. and Petersen, B. and Petrosyan, B. and Petrosyan, G. and Petrosyan, L. and Pfl{\"u}ger, J. and Pl{\"o}njes, E. and Poletto, L. and Pozniak, K. and Prat, E. and Proch, D. and Pucyk, P. and Radcliffe, P. and Redlin, H. and Rehlich, K. and Richter, M. and Roehrs, M. and Roensch, J. and Romaniuk, R. and Ross, M. and Rossbach, J. and Rybnikov, V. and Sachwitz, M. and Saldin, E. L. and Sandner, W. and Schlarb, H. and Schmidt, B. and Schmitz, M. and Schm{\"u}ser, P. and Schneider, J. R. and Schneidmiller, E. A. and Schnepp, S. and Schreiber, S. and Seidel, M. and Sertore, D. and Shabunov, A. V. and Simon, C. and Simrock, S. and Sombrowski, E. and Sorokin, A. A. and Spanknebel, P. and Spesyvtsev, R. and Staykov, L. and Steffen, B. and Stephan, F. and Stulle, F. and Thom, H. and Tiedtke, K. and Tischer, M. and Toleikis, S. and Treusch, R. and Trines, D. and Tsakov, I. and Vogel, E. and Weiland, T. and Weise, H. and Wellh{\"o}fer, M. and Wendt, M. and Will, I. and Winter, A. and Wittenburg, K. and Wurth, W. and Yeates, P. and Yurkov, M. V. and Zagorodnov, I. and Zapfe, K.},
  year = {2007},
  month = jun,
  journal = {Nature Photonics},
  volume = {1},
  number = {6},
  pages = {336--342},
  publisher = {Nature Publishing Group},
  issn = {1749-4893},
  doi = {10.1038/nphoton.2007.76},
  urldate = {2024-08-25},
  abstract = {We report results on the performance of a free-electron laser operating at a wavelength of 13.7~nm where unprecedented peak and average powers for a coherent extreme-ultraviolet radiation source have been measured. In the saturation regime, the peak energy approached 170~{\textmu}J for individual pulses, and the average energy per pulse reached 70~{\textmu}J. The pulse duration was in the region of 10~fs, and peak powers of 10~GW were achieved. At a pulse repetition frequency of 700 pulses per second, the average extreme-ultraviolet power reached 20~mW. The output beam also contained a significant contribution from odd harmonics of approximately 0.6\% and 0.03\% for the 3rd (4.6~nm) and the 5th (2.75~nm) harmonics, respectively. At 2.75~nm the 5th harmonic of the radiation reaches deep into the water window, a wavelength range that is crucially important for the investigation of biological samples.},
  copyright = {2007 Springer Nature Limited},
  langid = {english},
  keywords = {Applied and Technical Physics,general,Physics,Quantum Physics},
  file = {/Users/zain/Zotero/storage/22R5VZZ3/Ackermann et al. - 2007 - Operation of a free-electron laser from the extreme ultraviolet to the water window.pdf}
}

@article{agababyanMultiProcessorBasedFast2008,
  title = {Multi-{{Processor Based Fast Data Acquisition}} for a {{Free Electron Laser}} and {{Experiments}}},
  author = {Agababyan, A. and Asova, G. and Dimitrov, G. and Grygiel, G. and Fominykh, B. and Hensler, O. and Kammering, R. and Petrosyan, L. and Rehlich, K. and Rybnikov, V. and Trowitzsch, G. and Winde, M. and Wilksen, T.},
  year = {2008},
  journal = {IEEE Transactions on Nuclear Science},
  volume = {55},
  number = {1},
  pages = {256--260},
  doi = {10.1109/TNS.2007.913936}
}

@inproceedings{akibaOptunaNextgenerationHyperparameter2019,
  title = {Optuna: {{A Next-generation Hyperparameter Optimization Framework}}},
  shorttitle = {Optuna},
  booktitle = {Proceedings of the 25th {{ACM SIGKDD International Conference}} on {{Knowledge Discovery}} \& {{Data Mining}}},
  author = {Akiba, Takuya and Sano, Shotaro and Yanase, Toshihiko and Ohta, Takeru and Koyama, Masanori},
  year = {2019},
  month = jul,
  series = {{{KDD}} '19},
  pages = {2623--2631},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  doi = {10.1145/3292500.3330701},
  urldate = {2024-10-02},
  abstract = {The purpose of this study is to introduce new design-criteria for next-generation hyperparameter optimization software. The criteria we propose include (1) define-by-run API that allows users to construct the parameter search space dynamically, (2) efficient implementation of both searching and pruning strategies, and (3) easy-to-setup, versatile architecture that can be deployed for various purposes, ranging from scalable distributed computing to light-weight experiment conducted via interactive interface. In order to prove our point, we will introduce Optuna, an optimization software which is a culmination of our effort in the development of a next generation optimization software. As an optimization software designed with define-by-run principle, Optuna is particularly the first of its kind. We will present the design-techniques that became necessary in the development of the software that meets the above criteria, and demonstrate the power of our new design through experimental results and real world applications. Our software is available under the MIT license (https://github.com/pfnet/optuna/).},
  isbn = {978-1-4503-6201-6}
}

@book{AllStatistics,
  title = {All of {{Statistics}}},
  urldate = {2024-10-09},
  langid = {english},
  file = {/Users/zain/Zotero/storage/4E46WK9K/All of Statistics.pdf;/Users/zain/Zotero/storage/2LKIFE8E/978-0-387-21736-9.html}
}

@incollection{als-nielsenXraysTheirInteraction2011,
  title = {X-Rays and Their Interaction with Matter},
  booktitle = {Elements of {{Modern X}}-ray {{Physics}}},
  author = {{Als-Nielsen}, Jens and McMorrow, Des},
  year = {2011},
  pages = {1--28},
  publisher = {John Wiley \& Sons, Ltd},
  doi = {10.1002/9781119998365.ch1},
  abstract = {Summary This chapter contains sections titled: X-rays: waves and photons Scattering Absorption Refraction and reflection Coherence Magnetic interactions},
  chapter = {1},
  isbn = {978-1-119-99836-5},
  keywords = {absorption,coherence,magnetic interactions,scattering,X-rays}
}

@article{anscombeTransformationPoissonBinomial1948,
  title = {The {{Transformation}} of {{Poisson}}, {{Binomial}} and {{Negative-Binomial Data}}},
  author = {Anscombe, F. J.},
  year = {1948},
  month = dec,
  journal = {Biometrika},
  volume = {35},
  number = {3/4},
  eprint = {2332343},
  eprinttype = {jstor},
  pages = {246},
  issn = {00063444},
  doi = {10.2307/2332343},
  urldate = {2024-09-02},
  file = {/Users/zain/Zotero/storage/K2QJCFIN/Anscombe - 1948 - The Transformation of Poisson, Binomial and Negative-Binomial Data.pdf}
}

@misc{barry2020gamma,
  title = {Tim Barry: {{Gamma}}, Poisson, and Negative Binomial Distributions},
  author = {Barry, Tim},
  year = {2020}
}

@article{bartlettSquareRootTransformation1936,
  title = {The {{Square Root Transformation}} in {{Analysis}} of {{Variance}}},
  author = {Bartlett, M. S.},
  year = {1936},
  journal = {Supplement to the Journal of the Royal Statistical Society},
  volume = {3},
  number = {1},
  eprint = {2983678},
  eprinttype = {jstor},
  pages = {68--78},
  publisher = {[Oxford University Press, Royal Statistical Society]},
  issn = {1466-6162},
  doi = {10.2307/2983678},
  urldate = {2024-11-04},
  file = {/Users/zain/Zotero/storage/V8A7RR5A/Bartlett - 1936 - The Square Root Transformation in Analysis of Variance.pdf}
}

@article{bellLessonsNetflixPrize2007,
  title = {Lessons from the {{Netflix}} Prize Challenge},
  author = {Bell, Robert M. and Koren, Yehuda},
  year = {2007},
  month = dec,
  journal = {ACM SIGKDD Explorations Newsletter},
  volume = {9},
  number = {2},
  pages = {75--79},
  issn = {1931-0145, 1931-0153},
  doi = {10.1145/1345448.1345465},
  urldate = {2024-10-17},
  abstract = {This article outlines the overall strategy and summarizes a few key innovations of the team that won the first Netflix progress prize.},
  langid = {english},
  file = {/Users/zain/Zotero/storage/EISM3MWQ/Bell and Koren - 2007 - Lessons from the Netflix prize challenge.pdf}
}

@article{bergstraRandomSearchHyperParameter,
  title = {Random {{Search}} for {{Hyper-Parameter Optimization}}},
  author = {Bergstra, James and Bengio, Yoshua},
  abstract = {Grid search and manual search are the most widely used strategies for hyper-parameter optimization. This paper shows empirically and theoretically that randomly chosen trials are more efficient for hyper-parameter optimization than trials on a grid. Empirical evidence comes from a comparison with a large previous study that used grid search and manual search to configure neural networks and deep belief networks. Compared with neural networks configured by a pure grid search, we find that random search over the same domain is able to find models that are as good or better within a small fraction of the computation time. Granting random search the same computational budget, random search finds better models by effectively searching a larger, less promising configuration space. Compared with deep belief networks configured by a thoughtful combination of manual search and grid search, purely random search over the same 32-dimensional configuration space found statistically equal performance on four of seven data sets, and superior performance on one of seven. A Gaussian process analysis of the function from hyper-parameters to validation set performance reveals that for most data sets only a few of the hyper-parameters really matter, but that different hyper-parameters are important on different data sets. This phenomenon makes grid search a poor choice for configuring algorithms for new data sets. Our analysis casts some light on why recent ``High Throughput'' methods achieve surprising success---they appear to search through a large number of hyper-parameters because most hyper-parameters do not matter much. We anticipate that growing interest in large hierarchical models will place an increasing burden on techniques for hyper-parameter optimization; this work shows that random search is a natural baseline against which to judge progress in the development of adaptive (sequential) hyper-parameter optimization algorithms.},
  langid = {english},
  file = {/Users/zain/Zotero/storage/49P85UET/Bergstra et al. - Random Search for Hyper-Parameter Optimization.pdf}
}

@article{berteroImageDeblurringPoisson2009,
  title = {Image Deblurring with {{Poisson}} Data: From Cells to Galaxies},
  shorttitle = {Image Deblurring with {{Poisson}} Data},
  author = {Bertero, M and Boccacci, P and Desider{\`a}, G and Vicidomini, G},
  year = {2009},
  month = dec,
  journal = {Inverse Problems},
  volume = {25},
  number = {12},
  pages = {123006},
  issn = {0266-5611, 1361-6420},
  doi = {10.1088/0266-5611/25/12/123006},
  urldate = {2024-08-19},
  keywords = {Math,Poisson},
  file = {/Users/zain/Documents/Masters/Thesis/papers/JD-54.pdf}
}

@article{berteroImageDeblurringPoisson2009a,
  title = {Image Deblurring with {{Poisson}} Data: From Cells to Galaxies},
  shorttitle = {Image Deblurring with {{Poisson}} Data},
  author = {Bertero, M and Boccacci, P and Desider{\`a}, G and Vicidomini, G},
  year = {2009},
  month = dec,
  journal = {Inverse Problems},
  volume = {25},
  number = {12},
  pages = {123006},
  issn = {0266-5611, 1361-6420},
  doi = {10.1088/0266-5611/25/12/123006},
  urldate = {2024-09-25},
  abstract = {Image deblurring is an important topic in imaging science. In this review, we consider together fluorescence microscopy and optical/infrared astronomy because of two common features: in both cases the imaging system can be described, with a sufficiently good approximation, by a convolution operator, whose kernel is the so-called point-spread function (PSF); moreover, the data are affected by photon noise, described by a Poisson process. This statistical property of the noise, that is common also to emission tomography, is the basis of maximum likelihood and Bayesian approaches introduced in the mid eighties. From then on, a huge amount of literature has been produced on these topics. This review is a tutorial and a review of a relevant part of this literature, including some of our previous contributions. We discuss the mathematical modeling of the process of image formation and detection, and we introduce the so-called Bayesian paradigm that provides the basis of the statistical treatment of the problem. Next, we describe and discuss the most frequently used algorithms as well as other approaches based on a different description of the Poisson noise. We conclude with a review of other topics related to image deblurring such as boundary effect correction, space-variant PSFs, super-resolution, blind deconvolution and multiple-image deconvolution.},
  langid = {english},
  file = {/Users/zain/Zotero/storage/JWD7XXIK/Bertero et al. - 2009 - Image deblurring with Poisson data from cells to galaxies.pdf}
}

@book{binneyPhysicsQuantumMechanics2014,
  title = {The Physics of Quantum Mechanics},
  author = {Binney, James and Skinner, David Benjamin},
  year = {2014},
  publisher = {Oxford university press},
  address = {Oxford},
  isbn = {978-0-19-968857-9},
  langid = {english},
  lccn = {530.12},
  file = {/Users/zain/Zotero/storage/4SEUFXRE/Binney - The Physics of Quantum Mechanics.pdf}
}

@book{bishopDeepLearningFoundations2024,
  title = {Deep Learning: Foundations and Concepts},
  shorttitle = {Deep Learning},
  author = {Bishop, Christopher M. and Bishop, Hugh},
  year = {2024},
  publisher = {Springer},
  address = {Cham, Switzerland},
  doi = {10.1007/978-3-031-45468-4},
  abstract = {This book offers a comprehensive introduction to the central ideas that underpin deep learning. It is intended both for newcomers to machine learning and for those already experienced in the field. Covering key concepts relating to contemporary architectures and techniques, this essential book equips readers with a robust foundation for potential future specialization. The field of deep learning is undergoing rapid evolution, and therefore this book focusses on ideas that are likely to endure the test of time. The book is organized into numerous bite-sized chapters, each exploring a distinct topic, and the narrative follows a linear progression, with each chapter building upon content from its predecessors. This structure is well-suited to teaching a two-semester undergraduate or postgraduate machine learning course, while remaining equally relevant to those engaged in active research or in self-study. A full understanding of machine learning requires some mathematical background and so the book includes a self-contained introduction to probability theory. However, the focus of the book is on conveying a clear understanding of ideas, with emphasis on the real-world practical value of techniques rather than on abstract theory. Complex concepts are therefore presented from multiple complementary perspectives including textual descriptions, diagrams, mathematical formulae, and pseudo-code. Chris Bishop is a Technical Fellow at Microsoft and is the Director of Microsoft Research AI4Science. He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society. Hugh Bishop is an Applied Scientist at Wayve, a deep learning autonomous driving company in London, where he designs and trains deep neural networks. He completed his MPhil in Machine Learning and Machine Intelligence at Cambridge University. "Chris Bishop wrote a terrific textbook on neural networks in 1995 and has a deep knowledge of the field and its core ideas. His many years of experience in explaining neural networks have made him extremely skillful at presenting complicated ideas in the simplest possible way and it is a delight to see these skills applied to the revolutionary new developments in the field." -- Geoffrey Hinton "With the recent explosion of deep learning and AI as a research topic, and the quickly growing importance of AI applications, a modern textbook on the topic was badly needed. The "New Bishop" masterfully fills the gap, covering algorithms for supervised and unsupervised learning, modern deep learning architecture families, as well as how to apply all of this to various application areas." - Yann LeCun "This excellent and very educational book will bring the reader up to date with the main concepts and advances in deep learning with a solid anchoring in probability. These concepts are powering current industrial AI systems and are likely to form the basis of further advances towards artificial general intelligence." -- Yoshua Bengio},
  isbn = {978-3-031-45467-7},
  langid = {english},
  keywords = {Deep Learning},
  file = {/Users/zain/Documents/Masters/Thesis/books/Bishop and Bishop - 2024 - Deep learning foundations and concepts.pdf}
}

@book{bishopPatternRecognitionMachine2006,
  title = {Pattern Recognition and Machine Learning},
  author = {Bishop, Christopher M.},
  year = {2006},
  series = {Information Science and Statistics},
  publisher = {Springer},
  address = {New York},
  isbn = {978-0-387-31073-2},
  langid = {english},
  lccn = {006.4}
}

@article{buadesReviewImageDenoising2005,
  title = {A {{Review}} of {{Image Denoising Algorithms}}, with a {{New One}}},
  author = {Buades, A. and Coll, B. and Morel, J. M.},
  year = {2005},
  month = jan,
  journal = {Multiscale Modeling \& Simulation},
  volume = {4},
  number = {2},
  pages = {490--530},
  issn = {1540-3459, 1540-3467},
  doi = {10.1137/040616024},
  urldate = {2024-09-26},
  langid = {english},
  file = {/Users/zain/Zotero/storage/K4RFA553/Buades et al. - 2005 - A Review of Image Denoising Algorithms, with a New One.pdf}
}

@book{cardonaGeneralPrinciples1978,
  title = {General Principles},
  editor = {Cardona, Manuel and Ley, Lothar},
  year = {1978},
  series = {Photoemission in Solids / Ed. by {{M}}. {{Cardona}}},
  number = {1},
  publisher = {Springer},
  address = {Berlin},
  isbn = {978-3-662-30919-3 978-3-540-08685-7 978-0-387-08685-9},
  langid = {english},
  file = {/Users/zain/Zotero/storage/55YL465C/1978 - Photoemission in solids. 1 General principles.pdf}
}

@article{castronetoElectronicPropertiesGraphene2009,
  title = {The Electronic Properties of Graphene},
  author = {Castro Neto, A. H. and Guinea, F. and Peres, N. M. R. and Novoselov, K. S. and Geim, A. K.},
  year = {2009},
  month = jan,
  journal = {Reviews of Modern Physics},
  volume = {81},
  number = {1},
  pages = {109--162},
  issn = {0034-6861, 1539-0756},
  doi = {10.1103/RevModPhys.81.109},
  urldate = {2024-10-01},
  copyright = {http://link.aps.org/licenses/aps-default-license},
  langid = {english},
  file = {/Users/zain/Zotero/storage/TTGBDCVK/Castro Neto et al. - 2009 - The electronic properties of graphene.pdf}
}

@book{chiuStochasticGeometryIts2013,
  title = {Stochastic Geometry and Its Applications},
  editor = {Chiu, Sung Nok and Stoyan, Dietrich and Kendall, Wilfrid S. and Mecke, Joseph and Stoyan, Dietrich and Kendall, Wilfrid S. and Mecke, Joseph},
  year = {2013},
  series = {Wiley Series in Probability and Statistics},
  edition = {3. Aufl., 1. publ},
  publisher = {Wiley},
  address = {Chichester},
  isbn = {978-0-470-66481-0},
  langid = {english},
  file = {/Users/zain/Zotero/storage/3SX336MV/Chiu et al. - 2013 - Stochastic geometry and its applications.pdf}
}

@misc{cicek3DUNetLearning2016,
  title = {{{3D U-Net}}: {{Learning Dense Volumetric Segmentation}} from {{Sparse Annotation}}},
  shorttitle = {{{3D U-Net}}},
  author = {{\c C}i{\c c}ek, {\"O}zg{\"u}n and Abdulkadir, Ahmed and Lienkamp, Soeren S. and Brox, Thomas and Ronneberger, Olaf},
  year = {2016},
  month = jun,
  number = {arXiv:1606.06650},
  eprint = {1606.06650},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.1606.06650},
  urldate = {2024-09-19},
  abstract = {This paper introduces a network for volumetric segmentation that learns from sparsely annotated volumetric images. We outline two attractive use cases of this method: (1) In a semi-automated setup, the user annotates some slices in the volume to be segmented. The network learns from these sparse annotations and provides a dense 3D segmentation. (2) In a fully-automated setup, we assume that a representative, sparsely annotated training set exists. Trained on this data set, the network densely segments new volumetric images. The proposed network extends the previous u-net architecture from Ronneberger et al. by replacing all 2D operations with their 3D counterparts. The implementation performs on-the-fly elastic deformations for efficient data augmentation during training. It is trained end-to-end from scratch, i.e., no pre-trained network is required. We test the performance of the proposed method on a complex, highly variable 3D structure, the Xenopus kidney, and achieve good results for both use cases.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computer Vision and Pattern Recognition},
  file = {/Users/zain/Zotero/storage/TPN25ERK/Çiçek et al. - 2016 - 3D U-Net Learning Dense Volumetric Segmentation from Sparse Annotation.pdf;/Users/zain/Zotero/storage/J5CJEUP7/1606.html}
}

@article{correaTEMPUSTimepix4basedSystem2024,
  title = {{{TEMPUS}}, a {{Timepix4-based}} System for the Event-Based Detection of {{X-rays}}},
  author = {Correa, Jonathan and Ignatenko, Alexandr and Pennicard, David and Lange, Sabine and Fridman, Sergei and Karl, Sebastian and Lohse, Leon and Senfftleben, Bj{\"o}rn and Sergeev, Ilya and Velten, Sven and Prajapat, Deepak and Bocklage, Lars and Bromberger, Hubertus and Samartsev, Andrey and Chumakov, Aleksandr and R{\"u}ffer, Rudolf and Von Zanthier, Joachim and R{\"o}hlsberger, Ralf and Graafsma, Heinz},
  year = {2024},
  month = sep,
  journal = {Journal of Synchrotron Radiation},
  volume = {31},
  number = {5},
  pages = {1209--1216},
  issn = {1600-5775},
  doi = {10.1107/S1600577524005319},
  urldate = {2024-11-17},
  abstract = {TEMPUS is a new detector system being developed for photon science. It is based on the Timepix4 chip and, thus, it can be operated in two distinct modes: a photon-counting mode, which allows for conventional full-frame readout at rates up to 40\>kfps; and an event-driven time-stamping mode, which allows excellent time resolution in the nanosecond regime in measurements with moderate X-ray flux. In this paper, the initial prototype, a single-chip device, is introduced, and the readout system described. Moreover, and in order to evaluate its capabilities, some tests were performed at PETRA\>III and ESRF for which results are also presented.},
  file = {/Users/zain/Zotero/storage/5NLK232R/Correa et al. - 2024 - TEMPUS, a Timepix4-based system for the event-based detection of X-rays.pdf}
}

@inproceedings{dabovImageDenoisingBlockmatching2006,
  title = {Image Denoising with Block-Matching and {{3D}} Filtering},
  booktitle = {Electronic {{Imaging}} 2006},
  author = {Dabov, Kostadin and Foi, Alessandro and Katkovnik, Vladimir and Egiazarian, Karen},
  editor = {Dougherty, Edward R. and Astola, Jaakko T. and Egiazarian, Karen O. and Nasrabadi, Nasser M. and Rizvi, Syed A.},
  year = {2006},
  month = feb,
  pages = {606414},
  address = {San Jose, CA},
  doi = {10.1117/12.643267},
  urldate = {2024-09-25},
  abstract = {We present a novel approach to still image denoising based on e ective filtering in 3D transform domain by combining sliding-window transform processing with block-matching. We process blocks within the image in a sliding manner and utilize the block-matching concept by searching for blocks which are similar to the currently processed one. The matched blocks are stacked together to form a 3D array and due to the similarity between them, the data in the array exhibit high level of correlation. We exploit this correlation by applying a 3D decorrelating unitary transform and e ectively attenuate the noise by shrinkage of the transform coe!cients. The subsequent inverse 3D transform yields estimates of all matched blocks. After repeating this procedure for all image blocks in sliding manner, the final estimate is computed as weighed average of all overlapping blockestimates. A fast and e!cient algorithm implementing the proposed approach is developed. The experimental results show that the proposed method delivers state-of-art denoising performance, both in terms of objective criteria and visual quality.},
  langid = {english},
  file = {/Users/zain/Zotero/storage/HRS9WFVG/Dabov et al. - 2006 - Image denoising with block-matching and 3D filtering.pdf}
}

@article{dabovImageDenoisingSparse2007,
  title = {Image {{Denoising}} by {{Sparse}} 3-{{D Transform-Domain Collaborative Filtering}}},
  author = {Dabov, Kostadin and Foi, Alessandro and Katkovnik, Vladimir and Egiazarian, Karen},
  year = {2007},
  month = aug,
  journal = {IEEE Transactions on Image Processing},
  volume = {16},
  number = {8},
  pages = {2080--2095},
  issn = {1941-0042},
  doi = {10.1109/TIP.2007.901238},
  urldate = {2024-09-03},
  abstract = {We propose a novel image denoising strategy based on an enhanced sparse representation in transform domain. The enhancement of the sparsity is achieved by grouping similar 2D image fragments (e.g., blocks) into 3D data arrays which we call "groups." Collaborative Altering is a special procedure developed to deal with these 3D groups. We realize it using the three successive steps: 3D transformation of a group, shrinkage of the transform spectrum, and inverse 3D transformation. The result is a 3D estimate that consists of the jointly filtered grouped image blocks. By attenuating the noise, the collaborative filtering reveals even the finest details shared by grouped blocks and, at the same time, it preserves the essential unique features of each individual block. The filtered blocks are then returned to their original positions. Because these blocks are overlapping, for each pixel, we obtain many different estimates which need to be combined. Aggregation is a particular averaging procedure which is exploited to take advantage of this redundancy. A significant improvement is obtained by a specially developed collaborative Wiener filtering. An algorithm based on this novel denoising strategy and its efficient implementation are presented in full detail; an extension to color-image denoising is also developed. The experimental results demonstrate that this computationally scalable algorithm achieves state-of-the-art denoising performance in terms of both peak signal-to-noise ratio and subjective visual quality.},
  keywords = {3-D transform shrinkage,Adaptive grouping,block matching,Collaboration,Discrete cosine transforms,Energy resolution,Filtering,image denoising,Image denoising,Noise reduction,Signal processing,Signal processing algorithms,Signal resolution,sparsity,Spatial resolution},
  file = {/Users/zain/Documents/Masters/Thesis/papers/Dabov et al. - 2007 - Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering.pdf;/Users/zain/Zotero/storage/H64SLRFP/4271520.html}
}

@article{dabovImageDenoisingSparse2007b,
  title = {Image {{Denoising}} by {{Sparse}} 3-{{D Transform-Domain Collaborative Filtering}}},
  author = {Dabov, Kostadin and Foi, Alessandro and Katkovnik, Vladimir and Egiazarian, Karen},
  year = {2007},
  month = aug,
  journal = {IEEE Transactions on Image Processing},
  volume = {16},
  number = {8},
  pages = {2080--2095},
  issn = {1941-0042},
  doi = {10.1109/TIP.2007.901238},
  urldate = {2024-09-26},
  abstract = {We propose a novel image denoising strategy based on an enhanced sparse representation in transform domain. The enhancement of the sparsity is achieved by grouping similar 2D image fragments (e.g., blocks) into 3D data arrays which we call "groups." Collaborative Altering is a special procedure developed to deal with these 3D groups. We realize it using the three successive steps: 3D transformation of a group, shrinkage of the transform spectrum, and inverse 3D transformation. The result is a 3D estimate that consists of the jointly filtered grouped image blocks. By attenuating the noise, the collaborative filtering reveals even the finest details shared by grouped blocks and, at the same time, it preserves the essential unique features of each individual block. The filtered blocks are then returned to their original positions. Because these blocks are overlapping, for each pixel, we obtain many different estimates which need to be combined. Aggregation is a particular averaging procedure which is exploited to take advantage of this redundancy. A significant improvement is obtained by a specially developed collaborative Wiener filtering. An algorithm based on this novel denoising strategy and its efficient implementation are presented in full detail; an extension to color-image denoising is also developed. The experimental results demonstrate that this computationally scalable algorithm achieves state-of-the-art denoising performance in terms of both peak signal-to-noise ratio and subjective visual quality.},
  keywords = {3-D transform shrinkage,Adaptive grouping,block matching,Collaboration,Discrete cosine transforms,Energy resolution,Filtering,image denoising,Image denoising,Noise reduction,Signal processing,Signal processing algorithms,Signal resolution,sparsity,Spatial resolution},
  file = {/Users/zain/Zotero/storage/LA2PVNJV/Dabov et al. - 2007 - Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering.pdf;/Users/zain/Zotero/storage/9M264SI3/4271520.html}
}

@article{dellangelaTimeResolvedXray2015,
  title = {Time Resolved {{X-ray}} Absorption Spectroscopy in Condensed Matter: {{A}} Road Map to the Future},
  shorttitle = {Time Resolved {{X-ray}} Absorption Spectroscopy in Condensed Matter},
  author = {Dell'Angela, Martina and Parmigiani, Fulvio and Malvestuto, Marco},
  year = {2015},
  month = apr,
  journal = {Journal of Electron Spectroscopy and Related Phenomena},
  series = {Special {{Anniversary Issue}}: {{Volume}} 200},
  volume = {200},
  pages = {22--30},
  issn = {0368-2048},
  doi = {10.1016/j.elspec.2015.06.014},
  urldate = {2024-10-29},
  abstract = {Nowadays cutting edge femtosecond EUV and soft X-rays radiation sources are the driving force of groundbreaking time resolved X-ray spectroscopies. These new light sources are allowing pioneering experiments in the field of ultrafast phenomena and disclosing new insights about the physics of the out-of-equilibrium matter. Here we report an introductory and concise outlook about some possible perspectives in this field.},
  keywords = {Free electron laser,High harmonic generation laser source,Time resolved X-ray spectroscopies,X-ray femtosecond slicing sources},
  file = {/Users/zain/Zotero/storage/WFXBBP8T/S0368204815001449.html}
}

@book{demtroderAtomsMoleculesPhotons2010,
  title = {Atoms, {{Molecules}} and {{Photons}}},
  author = {Demtr{\"o}der, Wolfgang},
  year = {2010},
  publisher = {Springer Berlin Heidelberg},
  doi = {10.1007/978-3-642-10298-1}
}

@article{diwakarReviewCTImage2018,
  title = {A Review on {{CT}} Image Noise and Its Denoising},
  author = {Diwakar, Manoj and Kumar, Manoj},
  year = {2018},
  month = apr,
  journal = {Biomedical Signal Processing and Control},
  volume = {42},
  pages = {73--88},
  issn = {1746-8094},
  doi = {10.1016/j.bspc.2018.01.010},
  urldate = {2024-09-25},
  abstract = {CT imaging is widely used in medical science over the last decades. The process of CT image reconstruction depends on many physical measurements such as radiation dose, software/hardware. Due to statistical uncertainty in all physical measurements in Computed Tomography, the inevitable noise is introduced in CT images. Therefore, edge-preserving denoising methods are required to enhance the quality of CT images. However, there is a tradeoff between noise reduction and the preservation of actual medical relevant contents. Reducing the noise without losing the important features of the image such as edges, corners and other sharp structures, is a challenging task. Nevertheless, various techniques have been presented to suppress the noise from the CT scanned images. Each technique has their own assumptions, merits and limitations. This paper contains a survey of some significant work in the area of CT image denoising. Often, researchers face difficulty to understand the noise in CT images and also to select an appropriate denoising method that is specific to their purpose. Hence, a brief introduction about CT imaging, the characteristics of noise in CT images and the popular methods of CT image denoising are presented here. The merits and drawbacks of CT image denoising methods are also discussed.},
  keywords = {Anisotropic function,Computed Tomography,Image denoising,Isotropic function,Total variation},
  file = {/Users/zain/Zotero/storage/3XC9A27Y/Diwakar and Kumar - 2018 - A review on CT image noise and its denoising.pdf;/Users/zain/Zotero/storage/3VFZVZJS/S1746809418300107.html}
}

@inproceedings{drorYahooMusicDataset2012,
  title = {The {{Yahoo}}! {{Music Dataset}} and {{KDD-Cup}}'11},
  booktitle = {Proceedings of {{KDD Cup}} 2011},
  author = {Dror, Gideon and Koenigstein, Noam and Koren, Yehuda and Weimer, Markus},
  year = {2012},
  month = jun,
  pages = {3--18},
  publisher = {PMLR},
  issn = {1938-7228},
  urldate = {2024-10-17},
  abstract = {KDD-Cup 2011 challenged the community to identify user tastes in music by leveraging Yahoo! Music user ratings. The competition hosted two tracks, which were based on two datasets sampled from the raw data, including hundreds of millions of ratings. The underlying ratings were given to four types of musical items: tracks, albums, artists, and genres, forming a four level hierarchical taxonomy. The challenge started on March 15, 2011 and ended on June 30, 2011 attracting 2389 participants, 2100 of which were active by the end of the competition. The popularity of the challenge is related to the fact that learning a large scale recommender systems is a generic problem, highly relevant to the industry. In addition, the contest drew interest by introducing a number of scientific and technical challenges including dataset size, hierarchical structure of items, high resolution timestamps of ratings, and a non-conventional ranking-based task. This paper provides the organizers' account of the contest, including: a detailed analysis of the datasets, discussion of the contest goals and actual conduct, and lessons learned throughout the contest.},
  langid = {english},
  file = {/Users/zain/Zotero/storage/KL7LB7RB/Dror et al. - 2012 - The Yahoo! Music Dataset and KDD-Cup’11.pdf}
}

@article{einsteinUberErzeugungUnd1905,
  title = {{\"U}ber Einen Die {{Erzeugung}} Und {{Verwandlung}} Des {{Lichtes}} Betreffenden Heuristischen {{Gesichtspunkt}}},
  author = {Einstein, A.},
  year = {1905},
  journal = {Annalen der Physik},
  volume = {322},
  number = {6},
  pages = {132--148},
  doi = {10.1002/andp.19053220607}
}

@article{eskiciogluImageQualityMeasures1995,
  title = {Image Quality Measures and Their Performance},
  author = {Eskicioglu, A.M. and Fisher, P.S.},
  year = {1995},
  month = dec,
  journal = {IEEE Transactions on Communications},
  volume = {43},
  number = {12},
  pages = {2959--2965},
  issn = {1558-0857},
  doi = {10.1109/26.477498},
  urldate = {2024-10-25},
  abstract = {A number of quality measures are evaluated for gray scale image compression. They are all bivariate, exploiting the differences between corresponding pixels in the original and degraded images. It is shown that although some numerical measures correlate well with the observers' response for a given compression technique, they are not reliable for an evaluation across different techniques. A graphical measure called Hosaka plots, however, can be used to appropriately specify not only the amount, but also the type of degradation in reconstructed images.},
  keywords = {Biomedical imaging,Data compression,Degradation,Humans,Image coding,Image quality,Image reconstruction,Pixel,Transform coding},
  file = {/Users/zain/Zotero/storage/ZDWSR3HC/Eskicioglu and Fisher - 1995 - Image quality measures and their performance.pdf}
}

@article{faatzSimultaneousOperationTwo2016,
  title = {Simultaneous Operation of Two Soft X-Ray Free-Electron Lasers Driven by One Linear Accelerator},
  author = {Faatz, B. and Pl{\"o}njes, E. and Ackermann, S. and Agababyan, A. and Asgekar, V. and Ayvazyan, V. and Baark, S. and Baboi, N. and Balandin, V. and von Bargen, N. and Bican, Y. and Bilani, O. and B{\"o}dewadt, J. and B{\"o}hnert, M. and B{\"o}spflug, R. and Bonfigt, S. and Bolz, H. and Borges, F. and Borkenhagen, O. and Brachmanski, M. and Braune, M. and Brinkmann, A. and Brovko, O. and Bruns, T. and Castro, P. and Chen, J. and Czwalinna, M. K. and Damker, H. and Decking, W. and Degenhardt, M. and Delfs, A. and Delfs, T. and Deng, H. and Dressel, M. and Duhme, H.-T. and D{\"u}sterer, S. and Eckoldt, H. and Eislage, A. and Felber, M. and Feldhaus, J. and Gessler, P. and Gibau, M. and Golubeva, N. and Golz, T. and Gonschior, J. and Grebentsov, A. and Grecki, M. and Gr{\"u}n, C. and Grunewald, S. and Hacker, K. and H{\"a}nisch, L. and Hage, A. and Hans, T. and Hass, E. and Hauberg, A. and Hensler, O. and Hesse, M. and Heuck, K. and Hidvegi, A. and Holz, M. and Honkavaara, K. and H{\"o}ppner, H. and Ignatenko, A. and J{\"a}ger, J. and Jastrow, U. and Kammering, R. and Karstensen, S. and Kaukher, A. and Kay, H. and Keil, B. and Klose, K. and Kocharyan, V. and K{\"o}pke, M. and K{\"o}rfer, M. and Kook, W. and Krause, B. and Krebs, O. and Kreis, S. and Krivan, F. and Kuhlmann, J. and Kuhlmann, M. and Kube, G. and Laarmann, T. and Lechner, C. and Lederer, S. and Leuschner, A. and Liebertz, D. and Liebing, J. and Liedtke, A. and Lilje, L. and Limberg, T. and Lipka, D. and Liu, B. and Lorbeer, B. and Ludwig, K. and Mahn, H. and Marinkovic, G. and Martens, C. and Marutzky, F. and Maslocv, M. and Meissner, D. and Mildner, N. and Miltchev, V. and Molnar, S. and Mross, D. and M{\"u}ller, F. and Neumann, R. and Neumann, P. and N{\"o}lle, D. and Obier, F. and Pelzer, M. and Peters, H.-B. and Petersen, K. and Petrosyan, A. and Petrosyan, G. and Petrosyan, L. and Petrosyan, V. and Petrov, A. and Pfeiffer, S. and Piotrowski, A. and Pisarov, Z. and Plath, T. and Pototzki, P. and Prandolini, M. J. and Prenting, J. and Priebe, G. and Racky, B. and Ramm, T. and Rehlich, K. and Riedel, R. and Roggli, M. and R{\"o}hling, M. and {R{\"o}nsch-Schulenburg}, J. and Rossbach, J. and Rybnikov, V. and Sch{\"a}fer, J. and Schaffran, J. and Schlarb, H. and Schlesselmann, G. and Schl{\"o}sser, M. and Schmid, P. and Schmidt, C. and {Schmidt-F{\"o}hre}, F. and Schmitz, M. and Schneidmiller, E. and Sch{\"o}ps, A. and Scholz, M. and Schreiber, S. and Sch{\"u}tt, K. and Sch{\"u}tz, U. and {Schulte-Schrepping}, H. and Schulz, M. and Shabunov, A. and Smirnov, P. and Sombrowski, E. and Sorokin, A. and Sparr, B. and Spengler, J. and Staack, M. and Stadler, M. and Stechmann, C. and Steffen, B. and Stojanovic, N. and Sychev, V. and Syresin, E. and Tanikawa, T. and Tavella, F. and Tesch, N. and Tiedtke, K. and Tischer, M. and Treusch, R. and Tripathi, S. and Vagin, P. and Vetrov, P. and Vilcins, S. and Vogt, M. and Wagner, A. de Zubiaurre and Wamsat, T. and Weddig, H. and Weichert, G. and Weigelt, H. and Wentowski, N. and Wiebers, C. and Wilksen, T. and Willner, A. and Wittenburg, K. and Wohlenberg, T. and Wortmann, J. and Wurth, W. and Yurkov, M. and Zagorodnov, I. and Zemella, J.},
  year = {2016},
  month = jun,
  journal = {New Journal of Physics},
  volume = {18},
  number = {6},
  pages = {062002},
  publisher = {IOP Publishing},
  issn = {1367-2630},
  doi = {10.1088/1367-2630/18/6/062002},
  urldate = {2024-09-24},
  abstract = {Extreme-ultraviolet to x-ray free-electron lasers (FELs) in operation for scientific applications are up to now single-user facilities. While most FELs generate around 100 photon pulses per second, FLASH at DESY can deliver almost two orders of magnitude more pulses in this time span due to its superconducting accelerator technology. This makes the facility a prime candidate to realize the next step in FELs---dividing the electron pulse trains into several FEL lines and delivering photon pulses to several users at the same time. Hence, FLASH has been extended with a second undulator line and self-amplified spontaneous emission (SASE) is demonstrated in both FELs simultaneously. FLASH can now deliver MHz pulse trains to two user experiments in parallel with individually selected photon beam characteristics. First results of the capabilities of this extension are shown with emphasis on independent variation of wavelength, repetition rate, and photon pulse length.},
  langid = {english}
}

@article{fanBriefReviewImage2019,
  title = {Brief Review of Image Denoising Techniques},
  author = {Fan, Linwei and Zhang, Fan and Fan, Hui and Zhang, Caiming},
  year = {2019},
  month = jul,
  journal = {Visual Computing for Industry, Biomedicine, and Art},
  volume = {2},
  number = {1},
  pages = {7},
  issn = {2524-4442},
  doi = {10.1186/s42492-019-0016-7},
  urldate = {2024-09-25},
  abstract = {With the explosion in the number of digital images taken every day, the demand for more accurate and visually pleasing images is increasing. However, the images captured by modern cameras are inevitably degraded by noise, which leads to deteriorated visual image quality. Therefore, work is required to reduce noise without losing image features (edges, corners, and other sharp structures). So far, researchers have already proposed various methods for decreasing noise. Each method has its own advantages and disadvantages. In this paper, we summarize some important research in the field of image denoising. First, we give the formulation of the image denoising problem, and then we present several image denoising techniques. In addition, we discuss the characteristics of these techniques. Finally, we provide several promising directions for future research.},
  keywords = {Convolutional neural network,Image denoising,Low-rank,Non-local means,Sparse representation},
  file = {/Users/zain/Zotero/storage/VTD3IQ6F/Fan et al. - 2019 - Brief review of image denoising techniques.pdf;/Users/zain/Zotero/storage/9E43CQJR/s42492-019-0016-7.html}
}

@book{fellerIntroductionProbabilityTheory1991,
  title = {An Introduction to Probability Theory and Its Applications. {{Vol}}. 2},
  author = {Feller, William},
  year = {1991},
  series = {Wiley Series in Probability and Mathematical Statistics},
  edition = {2. ed.},
  volume = {2},
  publisher = {Wiley},
  address = {S.l..},
  isbn = {978-0-471-25709-7},
  langid = {english},
  file = {/Users/zain/Documents/Masters/Thesis/books/Feller-1968-vol2.pdf}
}

@book{fellerIntroductionProbabilityTheory1991a,
  title = {An Introduction to Probability Theory and Its Applications},
  author = {Feller, William},
  year = {1991},
  series = {Wiley Series in Probability and Mathematical Statistics},
  edition = {Third ed. rev},
  publisher = {J. Wiley},
  address = {New York Chichester Brisbane [etc.]},
  isbn = {978-0-471-25708-0},
  langid = {english},
  lccn = {519.2},
  keywords = {Math,Poisson,Statistics},
  file = {/Users/zain/Documents/Masters/Thesis/books/an-introduction-to-probability-theory-and-its-applications.pdf}
}

@book{foxQuantumOpticsIntroduction2006,
  title = {Quantum Optics: An Introduction},
  shorttitle = {Quantum Optics},
  author = {Fox, Mark},
  year = {2006},
  series = {Oxford Master Series in Physics},
  number = {15},
  publisher = {Oxford University Press},
  address = {Oxford},
  isbn = {978-0-19-856672-4},
  langid = {english},
  lccn = {535.15},
  file = {/Users/zain/Zotero/storage/SEPWEUJR/Fox - 2006 - Quantum optics an introduction.pdf}
}

@book{gelmanRegressionOtherStories2021,
  title = {Regression and Other Stories},
  author = {Gelman, Andrew and Hill, Jennifer and Vehtari, Aki},
  year = {2021},
  series = {Analytical Methods for Social Research},
  publisher = {Cambridge University Press},
  address = {Cambridge},
  doi = {10.1017/9781139161879},
  abstract = {Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting},
  isbn = {978-1-107-02398-7 978-1-139-16187-9 978-1-107-67651-0},
  langid = {english},
  file = {/Users/zain/Zotero/storage/PNICYRP5/Gelman et al. - 2021 - Regression and other stories.pdf}
}

@book{gerryIntroductoryQuantumOptics2004,
  title = {Introductory {{Quantum Optics}}},
  author = {Gerry, Christopher and Knight, Peter},
  year = {2004},
  publisher = {Cambridge University Press},
  address = {Cambridge},
  doi = {10.1017/CBO9780511791239},
  urldate = {2024-08-30},
  abstract = {This book provides an elementary introduction to the subject of quantum optics, the study of the quantum mechanical nature of light and its interaction with matter. The presentation is almost entirely concerned with the quantized electromagnetic field. Topics covered include single-mode field quantization in a cavity, quantization of multimode fields, quantum phase, coherent states, quasi-probability distribution in phase space, atom-field interactions, the Jaynes-Cummings model, quantum coherence theory, beam splitters and interferometers, dissipative interactions, nonclassical field states with squeezing etc., 'Schr{\"o}dinger cat' states, tests of local realism with entangled photons from down-conversion, experimental realizations of cavity quantum electrodynamics, trapped ions, decoherence, and some applications to quantum information processing, particularly quantum cryptography. The book contains many homework problems and an extensive bibliography. This text is designed for upper-level undergraduates taking courses in quantum optics who have already taken a course in quantum mechanics, and for first and second year graduate students.},
  isbn = {978-0-521-52735-4},
  file = {/Users/zain/Zotero/storage/HMV3R3R5/Gerry and Knight - Introductory Quantum Optics.pdf}
}

@book{goodfellowDeepLearning2016,
  title = {Deep Learning},
  author = {Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron},
  year = {2016},
  series = {Adaptive Computation and Machine Learning},
  publisher = {The MIT Press},
  address = {Cambridge, Massachusetts},
  isbn = {978-0-262-03561-3},
  lccn = {Q325.5 .G66 2016},
  keywords = {Autoencoder,Deep Learning,Machine learning},
  file = {/Users/zain/Documents/Masters/Thesis/books/Ian Goodfellow, Yoshua Bengio, Aaron Courville - Deep Learning (2017, MIT).pdf}
}

@article{gorlachQuantumopticalNatureHigh2020,
  title = {The Quantum-Optical Nature of High Harmonic Generation},
  author = {Gorlach, Alexey and Neufeld, Ofer and Rivera, Nicholas and Cohen, Oren and Kaminer, Ido},
  year = {2020},
  month = sep,
  journal = {Nature Communications},
  volume = {11},
  number = {1},
  pages = {4598},
  issn = {2041-1723},
  doi = {10.1038/s41467-020-18218-w},
  urldate = {2024-10-29},
  abstract = {Abstract             High harmonic generation (HHG) is an extremely nonlinear effect generating coherent broadband radiation and pulse durations reaching attosecond timescales. Conventional models of HHG that treat the driving and emitted fields classically are usually very successful but inherently cannot capture the quantum-optical nature of the process. Although prior work considered quantum HHG, it remains unknown in what conditions the spectral and statistical properties of the radiation depart considerably from the known phenomenology of HHG. The discovery of such conditions could lead to novel sources of attosecond light having squeezing and entanglement. Here, we present a fully-quantum theory of extreme nonlinear optics, predicting quantum effects that alter both the spectrum and photon statistics of HHG, thus departing from all previous approaches. We predict the emission of shifted frequency combs and identify spectral features arising from the breakdown of the dipole approximation for the emission. Our results show that each frequency component of HHG can be bunched and squeezed and that each emitted photon is a superposition of all frequencies in the spectrum, i.e., each photon is a comb. Our general approach is applicable to a wide range of nonlinear optical processes, paving the way towards novel quantum phenomena in extreme nonlinear optics.},
  langid = {english},
  file = {/Users/zain/Zotero/storage/MRZ3GG2I/Gorlach et al. - 2020 - The quantum-optical nature of high harmonic generation.pdf}
}

@article{greenwoodInquiryNatureFrequency1920,
  title = {An {{Inquiry}} into the {{Nature}} of {{Frequency Distributions Representative}} of {{Multiple Happenings}} with {{Particular Reference}} to the {{Occurrence}} of {{Multiple Attacks}} of {{Disease}} or of {{Repeated Accidents}}},
  author = {Greenwood, Major and Yule, G. Udny},
  year = {1920},
  journal = {Journal of the Royal Statistical Society},
  volume = {83},
  number = {2},
  eprint = {2341080},
  eprinttype = {jstor},
  pages = {255--279},
  publisher = {[Wiley, Royal Statistical Society]},
  issn = {0952-8385},
  doi = {10.2307/2341080},
  urldate = {2024-09-19},
  keywords = {Gamma-Poisson},
  file = {/Users/zain/Zotero/storage/9GR7PCYI/Greenwood and Yule - 1920 - An Inquiry into the Nature of Frequency Distributions Representative of Multiple Happenings with Par.pdf}
}

@book{grootCoreLevelSpectroscopy2008,
  title = {Core {{Level Spectroscopy}} of {{Solids}}},
  author = {de Groot, Frank and Kotani, Akio},
  year = {2008},
  publisher = {CRS Press},
  address = {NW, United States},
  isbn = {978-0-8493-9071-5},
  file = {/Users/zain/Zotero/storage/NZ6K777H/Groot and Kotani - 2008 - Core Level Spectroscopy of Solids.pdf}
}

@book{harocheExploringQuantumAtoms2006,
  title = {Exploring the Quantum: Atoms, Cavities and Photons},
  shorttitle = {Exploring the Quantum},
  author = {Haroche, S. and Raimond, J.-M.},
  year = {2006},
  series = {Oxford Graduate Texts},
  publisher = {Oxford University Press},
  address = {Oxford ; New York},
  isbn = {978-0-19-850914-1},
  lccn = {QC174.12 .H376 2006},
  keywords = {Quantum theory},
  annotation = {OCLC: ocm68770236},
  file = {/Users/zain/Zotero/storage/E89ISBM9/Haroche and Raimond - 2006 - Exploring the quantum atoms, cavities and photons.pdf}
}

@article{heberMultispectralTimeresolvedEnergy2022,
  title = {Multispectral Time-Resolved Energy--Momentum Microscopy Using High-Harmonic Extreme Ultraviolet Radiation},
  author = {Heber, Michael and Wind, Nils and Kutnyakhov, Dmytro and Pressacco, Federico and Arion, Tiberiu and Roth, Friedrich and Eberhardt, Wolfgang and Rossnagel, Kai},
  year = {2022},
  month = aug,
  journal = {Review of Scientific Instruments},
  volume = {93},
  number = {8},
  pages = {083905},
  issn = {0034-6748, 1089-7623},
  doi = {10.1063/5.0091003},
  urldate = {2024-08-19},
  abstract = {A 790-nm-driven high-harmonic generation source with a repetition rate of 6~kHz is combined with a toroidal-grating monochromator and a high-detection-efficiency photoelectron time-of-flight momentum microscope to enable time- and momentum-resolved photoemission spectroscopy over a spectral range of 23.6--45.5~eV with sub-100~fs time resolution. Three-dimensional (3D) Fermi surface mapping is demonstrated on graphene-covered Ir(111) with energy and momentum resolutions of {$\lessequivlnt$}100 meV and {$\lessequivlnt$}0.1 {\AA}-1, respectively. The tabletop experiment sets the stage for measuring the kz-dependent ultrafast dynamics of 3D electronic structure, including band structure, Fermi surface, and carrier dynamics in 3D materials as well as 3D orbital dynamics in molecular layers.},
  langid = {english},
  keywords = {ARPES,Dataset},
  file = {/Users/zain/Documents/Masters/Thesis/papers/JD-32.pdf}
}

@phdthesis{heberStudiesUltrafastDynamics2024,
  title = {Studies on Ultrafast Dynamics in Correlated Electron Systems with Time- and Angle-Resolved Photoemission Spectroscopy},
  author = {Heber, Michael},
  year = {2024},
  address = {Hamburg},
  school = {University of Hamburg}
}

@article{heimerlMultiphotonElectronEmission2024,
  title = {Multiphoton Electron Emission with Non-Classical Light},
  author = {Heimerl, Jonas and Mikhaylov, Alexander and Meier, Stefan and H{\"o}llerer, Henrick and Kaminer, Ido and Chekhova, Maria and Hommelhoff, Peter},
  year = {2024},
  month = jun,
  journal = {Nature Physics},
  volume = {20},
  number = {6},
  pages = {945--950},
  publisher = {Nature Publishing Group},
  issn = {1745-2481},
  doi = {10.1038/s41567-024-02472-6},
  urldate = {2024-08-28},
  abstract = {Photon number distributions of classical and non-classical light sources have been studied extensively, yet their impact on photoemission processes is largely unexplored. In this article, we present measurements of electron number distributions from metal needle tips illuminated with ultrashort light pulses with various photon quantum statistics. By varying the photon statistics of the exciting light field between classical (Poissonian) and quantum (super-Poissonian), we demonstrate that the measured electron distributions are changed substantially. Using single-mode bright squeezed vacuum light, we measure extreme statistics events with up to 65 electrons from one light pulse at a mean of 0.27 electrons per pulse---the likelihood for such an event equals 10-128 with Poissonian statistics. By changing the number of modes of the exciting bright squeezed vacuum, we can tailor the electron number distribution on demand. Most importantly, our results demonstrate that the photon statistics is imprinted from the driving light to the emitted electrons, opening the door to new sensor devices and to strong-field optics with quantum light and electrons.},
  copyright = {2024 The Author(s), under exclusive licence to Springer Nature Limited},
  langid = {english},
  keywords = {Nanophotonics and plasmonics,Nonlinear optics,Quantum optics},
  file = {/Users/zain/Zotero/storage/GNWJFTJC/Heimerl et al. - 2024 - Multiphoton electron emission with non-classical light.pdf}
}

@book{heisenbergPhysicalPrinciplesQuantum2009,
  title = {The Physical Principles of the Quantum Theory},
  author = {Heisenberg, Werner and Eckart, Carl and Hoyt, Frank C. and Heisenberg, Werner},
  year = {2009},
  series = {Dover Books on Physics},
  edition = {Nachdr.},
  publisher = {Dover Publ},
  address = {Mineola, NY},
  isbn = {978-0-486-60113-7},
  langid = {english}
}

@book{hohenesterNanoQuantumOptics2020,
  title = {Nano and {{Quantum Optics}}: {{An Introduction}} to {{Basic Principles}} and {{Theory}}},
  shorttitle = {Nano and {{Quantum Optics}}},
  author = {Hohenester, Ulrich},
  year = {2020},
  series = {Graduate {{Texts}} in {{Physics}}},
  publisher = {Springer International Publishing},
  address = {Cham},
  doi = {10.1007/978-3-030-30504-8},
  urldate = {2024-09-20},
  copyright = {http://www.springer.com/tdm},
  isbn = {978-3-030-30503-1 978-3-030-30504-8},
  langid = {english},
  file = {/Users/zain/Zotero/storage/S3F9Y3TU/Hohenester - 2020 - Nano and Quantum Optics An Introduction to Basic Principles and Theory.pdf}
}

@article{hoyerXarrayNDLabeled2017,
  title = {Xarray: {{N-D}} Labeled {{Arrays}} and {{Datasets}} in {{Python}}},
  shorttitle = {Xarray},
  author = {Hoyer, Stephan and Hamman, Joe},
  year = {2017},
  month = apr,
  journal = {Journal of Open Research Software},
  volume = {5},
  number = {1},
  pages = {10},
  issn = {2049-9647},
  doi = {10.5334/jors.148},
  urldate = {2024-09-02},
  copyright = {http://creativecommons.org/licenses/by/4.0},
  file = {/Users/zain/Zotero/storage/2JKHINX3/Hoyer and Hamman - 2017 - xarray N-D labeled Arrays and Datasets in Python.pdf}
}

@article{huberRobustEstimationLocation,
  title = {Robust {{Estimation}} of a {{Location Parameter}}},
  author = {Huber, Peter J.},
  urldate = {2024-11-21},
  abstract = {This paper contains a new approach toward a theory of robust estimation; it treats in detail the asymptotic theory of estimating a location parameter for contaminated normal distributions, and exhibits estimators--intermediaries between sample mean and sample median--that are asymptotically most robust (in a sense to be specified) among all translation invariant estimators. For the general background, see Tukey (1960) (p. 448 ff.) Let \$x\_1, {\textbackslash}cdots, x\_n\$ be independent random variables with common distribution function \$F(t - {\textbackslash}xi)\$. The problem is to estimate the location parameter \${\textbackslash}xi\$, but with the complication that the prototype distribution \$F(t)\$ is only approximately known. I shall primarily be concerned with the model of indeterminacy \$F = (1 - {\textbackslash}epsilon){\textbackslash}Phi + {\textbackslash}epsilon H\$, where \$0 {\textbackslash}leqq {\textbackslash}epsilon {$<$} 1\$ is a known number, \${\textbackslash}Phi(t) = (2{\textbackslash}pi){\textasciicircum}\{-{\textbackslash}frac\{1\}\{2\}\} {\textbackslash}int{\textasciicircum}t\_\{-{\textbackslash}infty\} {\textbackslash}exp(-{\textbackslash}frac\{1\}\{2\}s{\textasciicircum}2) ds\$ is the standard normal cumulative and \$H\$ is an unknown contaminating distribution. This model arises for instance if the observations are assumed to be normal with variance 1, but a fraction \${\textbackslash}epsilon\$ of them is affected by gross errors. Later on, I shall also consider other models of indeterminacy, e.g., \${\textbackslash}sup\_t {\textbar}F(t) - {\textbackslash}Phi(t){\textbar} {\textbackslash}leqq {\textbackslash}epsilon\$. Some inconvenience is caused by the fact that location and scale parameters are not uniquely determined: in general, for fixed \${\textbackslash}epsilon\$, there will be several values of \${\textbackslash}xi\$ and \${\textbackslash}sigma\$ such that \${\textbackslash}sup\_t{\textbar}F(t) - {\textbackslash}Phi((t - {\textbackslash}xi)/{\textbackslash}sigma){\textbar} {\textbackslash}leqq {\textbackslash}epsilon\$, and similarly for the contaminated case. Although this inherent and unavoidable indeterminacy is small if \${\textbackslash}epsilon\$ is small and is rather irrelevant for practical purposes, it poses awkward problems for the theory, especially for optimality questions. To remove this difficulty, one may either (i) restrict attention to symmetric distributions, and estimate the location of the center of symmetry (this works for \${\textbackslash}xi\$ but not for \${\textbackslash}sigma\$); or (ii) one may define the parameter to be estimated in terms of the estimator itself, namely by its asymptotic value for sample size \$n {\textbackslash}rightarrow {\textbackslash}infty\$; or (iii) one may define the parameters by arbitrarily chosen functionals of the distribution (e.g., by the expectation, or the median of \$F\$). All three possibilities have unsatisfactory aspects, and I shall usually choose the variant which is mathematically most convenient. It is interesting to look back to the very origin of the theory of estimation, namely to Gauss and his theory of least squares. Gauss was fully aware that his main reason for assuming an underlying normal distribution and a quadratic loss function was mathematical, i.e., computational, convenience. In later times, this was often forgotten, partly because of the central limit theorem. However, if one wants to be honest, the central limit theorem can at most explain why many distributions occurring in practice are approximately normal. The stress is on the word "approximately." This raises a question which could have been asked already by Gauss, but which was, as far as I know, only raised a few years ago (notably by Tukey): What happens if the true distribution deviates slightly from the assumed normal one? As is now well known, the sample mean then may have a catastrophically bad performance: seemingly quite mild deviations may already explode its variance. Tukey and others proposed several more robust substitutes--trimmed means, Winsorized means, etc.--and explored their performance for a few typical violations of normality. A general theory of robust estimation is still lacking; it is hoped that the present paper will furnish the first few steps toward such a theory. At the core of the method of least squares lies the idea to minimize the sum of the squared "errors," that is, to adjust the unknown parameters such that the sum of the squares of the differences between observed and computed values is minimized. In the simplest case, with which we are concerned here, namely the estimation of a location parameter, one has to minimize the expression \${\textbackslash}sum\_i (x\_i - T){\textasciicircum}2\$; this is of course achieved by the sample mean \$T = {\textbackslash}sum\_i x\_i/n\$. I should like to emphasize that no loss function is involved here; I am only describing how the least squares estimator is defined, and neither the underlying family of distributions nor the true value of the parameter to be estimated enters so far. It is quite natural to ask whether one can obtain more robustness by minimizing another function of the errors than the sum of their squares. We shall therefore concentrate our attention to estimators that can be defined by a minimum principle of the form (for a location parameter): \$T = T\_n(x\_1, {\textbackslash}cdots, x\_n) minimizes {\textbackslash}sum\_i {\textbackslash}rho(x\_i - T),\$ {\textbackslash}begin\{equation*\} {\textbackslash}tag\{M\} where {\textbackslash}rho is a non-constant function. {\textbackslash}end\{equation*\} Of course, this definition generalizes at once to more general least squares type problems, where several parameters have to be determined. This class of estimators contains in particular (i) the sample mean \$({\textbackslash}rho(t) = t{\textasciicircum}2)\$, (ii) the sample median \$({\textbackslash}rho(t) = {\textbar}t{\textbar})\$, and more generally, (iii) all maximum likelihood estimators \$({\textbackslash}rho(t) = -{\textbackslash}log f(t)\$, where \$f\$ is the assumed density of the untranslated distribution). These (\$M\$)-estimators, as I shall call them for short, have rather pleasant asymptotic properties; sufficient conditions for asymptotic normality and an explicit expression for their asymptotic variance will be given. How should one judge the robustness of an estimator \$T\_n(x) = T\_n(x\_1, {\textbackslash}cdots, x\_n)\$? Since ill effects from contamination are mainly felt for large sample sizes, it seems that one should primarily optimize large sample robustness properties. Therefore, a convenient measure of robustness for asymptotically normal estimators seems to be the supremum of the asymptotic variance \$(n {\textbackslash}rightarrow {\textbackslash}infty)\$ when \$F\$ ranges over some suitable set of underlying distributions, in particular over the set of all \$F = (1 - {\textbackslash}epsilon){\textbackslash}Phi + {\textbackslash}epsilon H\$ for fixed \${\textbackslash}epsilon\$ and symmetric \$H\$. On second thought, it turns out that the asymptotic variance is not only easier to handle, but that even for moderate values of \$n\$ it is a better measure of performance than the actual variance, because (i) the actual variance of an estimator depends very much on the behavior of the tails of \$H\$, and the supremum of the actual variance is infinite for any estimator whose value is always contained in the convex hull of the observations. (ii) If an estimator is asymptotically normal, then the important central part of its distribution and confidence intervals for moderate confidence levels can better be approximated in terms of the asymptotic variance than in terms of the actual variance. If we adopt this measure of robustness, and if we restrict attention to (\$M\$)-estimators, then it will be shown that the most robust estimator is uniquely determined and corresponds to the following \${\textbackslash}rho:{\textbackslash}rho(t) = {\textbackslash}frac\{1\}\{2\}t{\textasciicircum}2\$ for \${\textbar}t{\textbar} {$<$} k, {\textbackslash}rho(t) = k{\textbar}t{\textbar} - {\textbackslash}frac\{1\}\{2\}k{\textasciicircum}2\$ for \${\textbar}t{\textbar} {\textbackslash}geqq k\$, with \$k\$ depending on \${\textbackslash}epsilon\$. This estimator is most robust even among all translation invariant estimators. Sample mean \$(k = {\textbackslash}infty)\$ and sample median \$(k = 0)\$ are limiting cases corresponding to \${\textbackslash}epsilon = 0\$ and \${\textbackslash}epsilon = 1\$, respectively, and the estimator is closely related and asymptotically equivalent to Winsorizing. I recall the definition of Winsorizing: assume that the observations have been ordered, \$x\_1 {\textbackslash}leqq x\_2 {\textbackslash}leqq {\textbackslash}cdots {\textbackslash}leqq x\_n\$, then the statistic \$T = n{\textasciicircum}\{-1\}(gx\_\{g + 1\} + x\_\{g + 1\} + x\_\{g + 2\} + {\textbackslash}cdots + x\_\{n - h\} + hx\_\{n - h\})\$ is called the Winsorized mean, obtained by Winsorizing the \$g\$ leftmost and the \$h\$ rightmost observations. The above most robust (\$M\$)-estimators can be described by the same formula, except that in the first and in the last summand, the factors \$x\_\{g + 1\}\$ and \$x\_\{n - h\}\$ have to be replaced by some numbers \$u, v\$ satisfying \$x\_g {\textbackslash}leqq u {\textbackslash}leqq x\_\{g + 1\}\$ and \$x\_\{n - h\} {\textbackslash}leqq v {\textbackslash}leqq x\_\{n - h + 1\}\$, respectively; \$g, h, u\$ and \$v\$ depend on the sample. In fact, this (\$M\$)-estimator is the maximum likelihood estimator corresponding to a unique least favorable distribution \$F\_0\$ with density \$f\_0(t) = (1 - {\textbackslash}epsilon)(2{\textbackslash}pi){\textasciicircum}\{-{\textbackslash}frac\{1\}\{2\}\}e{\textasciicircum}\{-{\textbackslash}rho(t)\}\$. This \$f\_0\$ behaves like a normal density for small \$t\$, like an exponential density for large \$t\$. At least for me, this was rather surprising--I would have expected an \$f\_0\$ with much heavier tails. This result is a particular case of a more general one that can be stated roughly as follows: Assume that \$F\$ belongs to some convex set \$C\$ of distribution functions. Then the most robust (\$M\$)-estimator for the set \$C\$ coincides with the maximum likelihood estimator for the unique \$F\_0 {\textbackslash}varepsilon C\$ which has the smallest Fisher information number \$I(F) = {\textbackslash}int (f'/f){\textasciicircum}2f dt\$ among all \$F {\textbackslash}varepsilon C\$. Miscellaneous related problems will also be treated: the case of non-symmetric contaminating distributions; the most robust estimator for the model of indeterminacy \${\textbackslash}sup\_t{\textbar}F(t) - {\textbackslash}Phi(t){\textbar} {\textbackslash}leqq {\textbackslash}epsilon\$; robust estimation of a scale parameter; how to estimate location, if scale and \${\textbackslash}epsilon\$ are unknown; numerical computation of the estimators; more general estimators, e.g., minimizing \${\textbackslash}sum\_\{i {$<$} j\} {\textbackslash}rho(x\_i - T, x\_j - T)\$, where \${\textbackslash}rho\$ is a function of two arguments. Questions of small sample size theory will not be touched in this paper.},
  file = {/Users/zain/Zotero/storage/SUY6FJTJ/Huber - Robust Estimation of a Location Parameter.pdf}
}

@article{hunterMatplotlib2DGraphics2007,
  title = {Matplotlib: {{A 2D Graphics Environment}}},
  shorttitle = {Matplotlib},
  author = {Hunter, John D.},
  year = {2007},
  month = may,
  journal = {Computing in Science \& Engineering},
  volume = {9},
  number = {3},
  pages = {90--95},
  issn = {1558-366X},
  doi = {10.1109/MCSE.2007.55},
  urldate = {2024-09-02},
  abstract = {Matplotlib is a 2D graphics package used for Python for application development, interactive scripting,and publication-quality image generation across user interfaces and operating systems},
  keywords = {application development,Computer languages,Equations,Graphical user interfaces,Graphics,Image generation,Interpolation,Operating systems,Packaging,Programming profession,Python,scientific programming,scripting languages,User interfaces},
  file = {/Users/zain/Zotero/storage/DFMDQIQX/4160265.html}
}

@article{jagutzkiBroadapplicationMicrochannelplateDetector2002,
  title = {A Broad-Application Microchannel-Plate Detector System for Advanced Particle or Photon Detection Tasks: Large Area Imaging, Precise Multi-Hit Timing Information and High Detection Rate},
  shorttitle = {A Broad-Application Microchannel-Plate Detector System for Advanced Particle or Photon Detection Tasks},
  author = {Jagutzki, O and Mergel, V and {Ullmann-Pfleger}, K and Spielberger, L and Spillmann, U and D{\"o}rner, R and {Schmidt-B{\"o}cking}, H},
  year = {2002},
  month = jan,
  journal = {Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment},
  volume = {477},
  number = {1-3},
  pages = {244--249},
  issn = {01689002},
  doi = {10.1016/S0168-9002(01)01839-3},
  urldate = {2024-10-26},
  abstract = {New applications for single particle and photon detection in many fields require both large area imaging performance and precise time information on each detected particle. Moreover, a very high data acquisition rate is desirable for most applications and eventually the detection and imaging of more than one particle arriving within a microsecond is required. Commercial CCD systems lack the timing information whereas other electronic microchannel plate (MCP) read-out schemes usually suffer from a low acquisition rate and complicated and sometimes costly read-out electronics. We have designed and tested a complete imaging system consisting of an MCP position readout with helical wire delaylines, single-unit amplifier box and PC-controlled time-to-digital converter (TDC) readout. The system is very flexible and can detect and analyse position and timing information at single particle rates beyond 1 MHz. Alternatively, multihit events can be collected and analysed at about 20 kHz rate. We discuss the advantages and applications of this technique and then focus on the detector's ability to detect and analyse multiple hits. r 2002 Elsevier Science B.V. All rights reserved.},
  copyright = {https://www.elsevier.com/tdm/userlicense/1.0/},
  langid = {english},
  file = {/Users/zain/Zotero/storage/29CX3AXI/Jagutzki et al. - 2002 - A broad-application microchannel-plate detector system for advanced particle or photon detection tas.pdf}
}

@book{jamesIntroductionStatisticalLearning2013,
  title = {An {{Introduction}} to {{Statistical Learning}}},
  author = {James, Gareth and Witten, Daniela and Hastie, Trevor and Tibshirani, Robert},
  year = {2013},
  series = {Springer {{Texts}} in {{Statistics}}},
  volume = {103},
  publisher = {Springer New York},
  address = {New York, NY},
  doi = {10.1007/978-1-4614-7138-7},
  urldate = {2024-10-08},
  copyright = {http://www.springer.com/tdm},
  isbn = {978-1-4614-7137-0 978-1-4614-7138-7},
  langid = {english},
  file = {/Users/zain/Zotero/storage/GD7DKVMU/James et al. - 2013 - An Introduction to Statistical Learning.pdf}
}

@article{kimDeepLearningbasedStatistical2021,
  title = {Deep Learning-Based Statistical Noise Reduction for Multidimensional Spectral Data},
  author = {Kim, Younsik and Oh, Dongjin and Huh, Soonsang and Song, Dongjoon and Jeong, Sunbeom and Kwon, Junyoung and Kim, Minsoo and Kim, Donghan and Ryu, Hanyoung and Jung, Jongkeun and Kyung, Wonshik and Sohn, Byungmin and Lee, Suyoung and Hyun, Jounghoon and Lee, Yeonghoon and Kim, Yeongkwan and Kim, Changyoung},
  year = {2021},
  month = jul,
  journal = {Review of Scientific Instruments},
  volume = {92},
  number = {7},
  pages = {073901},
  issn = {0034-6748},
  doi = {10.1063/5.0054920},
  urldate = {2024-08-16},
  abstract = {In spectroscopic experiments, data acquisition in multi-dimensional phase space may require long acquisition time, owing to the large phase space volume to be covered. In such a case, the limited time available for data acquisition can be a serious constraint for experiments in which multidimensional spectral data are acquired. Here, taking angle-resolved photoemission spectroscopy (ARPES) as an example, we demonstrate a denoising method that utilizes deep learning as an intelligent way to overcome the constraint. With readily available ARPES data and random generation of training datasets, we successfully trained the denoising neural network without overfitting. The denoising neural network can remove the noise in the data while preserving its intrinsic information. We show that the denoising neural network allows us to perform a similar level of second-derivative and line shape analysis on data taken with two orders of magnitude less acquisition time. The importance of our method lies in its applicability to any multidimensional spectral data that are susceptible to statistical noise.},
  keywords = {ARPES,CNN,Deep Learning,Physics},
  file = {/Users/zain/Documents/Masters/Thesis/papers/Kim et al. - 2021 - Deep learning-based statistical noise reduction for multidimensional spectral data.pdf}
}

@misc{kingmaAdamMethodStochastic2017,
  title = {Adam: {{A Method}} for {{Stochastic Optimization}}},
  shorttitle = {Adam},
  author = {Kingma, Diederik P. and Ba, Jimmy},
  year = {2017},
  month = jan,
  number = {arXiv:1412.6980},
  eprint = {1412.6980},
  publisher = {arXiv},
  doi = {10.48550/arXiv.1412.6980},
  urldate = {2024-11-23},
  abstract = {We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Machine Learning},
  file = {/Users/zain/Zotero/storage/EE97PESZ/Kingma and Ba - 2017 - Adam A Method for Stochastic Optimization.pdf;/Users/zain/Zotero/storage/EMN2LMEM/1412.html}
}

@article{knipferDeepLearningbasedSpatiotemporal2024,
  title = {Deep Learning-Based Spatiotemporal Multi-Event Reconstruction for Delay Line Detectors},
  author = {Knipfer, Marco and Meier, Stefan and Volk, Tobias and Heimerl, Jonas and Hommelhoff, Peter and Gleyzer, Sergei},
  year = {2024},
  month = apr,
  journal = {Machine Learning: Science and Technology},
  volume = {5},
  number = {2},
  pages = {025019},
  publisher = {IOP Publishing},
  issn = {2632-2153},
  doi = {10.1088/2632-2153/ad3d2d},
  urldate = {2024-08-28},
  abstract = {Accurate observation of two or more particles within a very narrow time window has always been a challenge in modern physics. It creates the possibility of correlation experiments, such as the ground-breaking Hanbury Brown--Twiss experiment, leading to new physical insights. For low-energy electrons, one possibility is to use a Microchannel plate with subsequent delay lines for the readout of the incident particle hits, a setup called a Delay Line Detector. The spatial and temporal coordinates of more than one particle can be fully reconstructed outside a region called the dead radius. For interesting events, where two electrons are close in space and time, the determination of the individual positions of the electrons requires elaborate peak finding algorithms. While classical methods work well with single particle hits, they fail to identify and reconstruct events caused by multiple nearby particles. To address this challenge, we present a new spatiotemporal machine learning model to identify and reconstruct the position and time of such multi-hit particle signals. This model achieves a much better resolution for nearby particle hits compared to the classical approach, removing some of the artifacts and reducing the dead radius a factor of eight. We show that machine learning models can be effective in improving the spatiotemporal performance of delay line detectors.},
  langid = {english},
  file = {/Users/zain/Zotero/storage/JAIA675W/Knipfer et al. - 2024 - Deep learning-based spatiotemporal multi-event reconstruction for delay line detectors.pdf}
}

@article{kondratenkoGENERATINGCOHERENTRADIATION1980,
  title = {{{GENERATING OF COHERENT RADIATION BY A RELATIVISTIC ELECTRON BEAM IN AN ONDULATOR}}},
  author = {Kondratenko, A. M. and Saldin, E. L.},
  year = {1980},
  journal = {Part. Accel.},
  volume = {10},
  pages = {207--216},
  keywords = {BEAM DYNAMICS,BEAM INSTABILITY,BEAM: ELECTRON,COHERENT RADIATION: EMISSION,ELECTRON: BEAM,EMISSION: COHERENT RADIATION,MAGNET,NUMERICAL CALCULATIONS},
  file = {/Users/zain/Zotero/storage/AD7REEUN/Kondratenko and Saldin - 1980 - GENERATING OF COHERENT RADIATION BY A RELATIVISTIC ELECTRON BEAM IN AN ONDULATOR.pdf}
}

@misc{krullNoise2VoidLearningDenoising2018,
  title = {{{Noise2Void}} - {{Learning Denoising}} from {{Single Noisy Images}}},
  author = {Krull, Alexander and Buchholz, Tim-Oliver and Jug, Florian},
  year = {2018},
  publisher = {arXiv},
  doi = {10.48550/ARXIV.1811.10980},
  urldate = {2024-11-25},
  abstract = {The field of image denoising is currently dominated by discriminative deep learning methods that are trained on pairs of noisy input and clean target images. Recently it has been shown that such methods can also be trained without clean targets. Instead, independent pairs of noisy images can be used, in an approach known as Noise2Noise (N2N). Here, we introduce Noise2Void (N2V), a training scheme that takes this idea one step further. It does not require noisy image pairs, nor clean target images. Consequently, N2V allows us to train directly on the body of data to be denoised and can therefore be applied when other methods cannot. Especially interesting is the application to biomedical image data, where the acquisition of training targets, clean or noisy, is frequently not possible. We compare the performance of N2V to approaches that have either clean target images and/or noisy image pairs available. Intuitively, N2V cannot be expected to outperform methods that have more information available during training. Still, we observe that the denoising performance of Noise2Void drops in moderation and compares favorably to training-free denoising methods.},
  copyright = {arXiv.org perpetual, non-exclusive license},
  keywords = {Computer Vision and Pattern Recognition (cs.CV),FOS: Computer and information sciences}
}

@misc{kutnyakhovMultidimensionalPhotoemissionSpectra2024,
  title = {Multidimensional Photoemission Spectra of {{Gd}}/{{W}}(110)},
  author = {Kutnyakhov, Dmytro},
  year = {2024},
  month = feb,
  publisher = {Zenodo},
  doi = {10.5281/ZENODO.10658470},
  urldate = {2024-10-25},
  abstract = {Pump-probe multidimensional photoemission spectroscopy of gadolinium thin film on tungsten single crystal (Gd/W(110)) measured using an electron momentum~microscope (HEXTOF) at the PG2 beamline at Free Electron Laser FLASH/DESY, Hamburg. The single-event data (in single\_event\_data.zip file) zip file contains data for analysis - analysis\_data:~ runs 44824, 44825, 44826 and 44827 - trMM and data for calibration - calibration\_data: 44762 - chessy sample for PEEM mode FoV calibration 44797 - energy calibration 44798 and 44799 - optical spot profile Additionally, metadata information for every run is available in corresponding JSON files. ~ This dataset is to be used as an example dataset for tutorial with backend for handling photoelectron resolved datastreams:~ https://github.com/OpenCOMPES/sed},
  copyright = {Creative Commons Attribution 4.0 International}
}

@article{kutnyakhovTimeMomentumresolvedPhotoemission2020,
  title = {Time- and Momentum-Resolved Photoemission Studies Using Time-of-Flight Momentum Microscopy at a Free-Electron Laser},
  author = {Kutnyakhov, D. and Xian, R. P. and Dendzik, M. and Heber, M. and Pressacco, F. and Agustsson, S. Y. and Wenthaus, L. and Meyer, H. and Gieschen, S. and Mercurio, G. and Benz, A. and B{\"u}hlman, K. and D{\"a}ster, S. and Gort, R. and Curcio, D. and Volckaert, K. and Bianchi, M. and Sanders, {\relax Ch}. and Miwa, J. A. and Ulstrup, S. and Oelsner, A. and Tusche, C. and Chen, Y.-J. and Vasilyev, D. and Medjanik, K. and Brenner, G. and Dziarzhytski, S. and Redlin, H. and Manschwetus, B. and Dong, S. and Hauer, J. and Rettig, L. and Diekmann, F. and Rossnagel, K. and Demsar, J. and Elmers, H.-J. and Hofmann, {\relax Ph}. and Ernstorfer, R. and Sch{\"o}nhense, G. and Acremann, Y. and Wurth, W.},
  year = {2020},
  month = jan,
  journal = {Review of Scientific Instruments},
  volume = {91},
  number = {1},
  pages = {013109},
  issn = {0034-6748, 1089-7623},
  doi = {10.1063/1.5118777},
  urldate = {2024-08-19},
  abstract = {Time-resolved photoemission with ultrafast pump and probe pulses is an emerging technique with wide application potential. Real-time recording of nonequilibrium electronic processes, transient states in chemical reactions, or the interplay of electronic and structural dynamics offers fascinating opportunities for future research. Combining valence-band and core-level spectroscopy with photoelectron diffraction for electronic, chemical, and structural analyses requires few 10 fs soft X-ray pulses with some 10 meV spectral resolution, which are currently available at high repetition rate free-electron lasers. We have constructed and optimized a versatile setup commissioned at FLASH/PG2 that combines free-electron laser capabilities together with a multidimensional recording scheme for photoemission studies. We use a full-field imaging momentum microscope with time-of-flight energy recording as the detector for mapping of 3D band structures in (kx, ky, E) parameter space with unprecedented efficiency. Our instrument can image full surface Brillouin zones with up to 7 {\AA}-1 diameter in a binding-energy range of several eV, resolving about 2.5 {\texttimes} 105 data voxels simultaneously. Using the ultrafast excited state dynamics in the van der Waals semiconductor WSe2 measured at photon energies of 36.5 eV and 109.5 eV, we demonstrate an experimental energy resolution of 130 meV, a momentum resolution of 0.06 {\AA}-1, and a system response function of 150 fs.},
  langid = {english},
  keywords = {ARPES,HEXTOF},
  file = {/Users/zain/Documents/Masters/Thesis/papers/JD-41.pdf}
}

@article{ladislaswizaMicrochannelPlateDetectors1979,
  title = {Microchannel Plate Detectors},
  author = {Ladislas Wiza, Joseph},
  year = {1979},
  month = jun,
  journal = {Nuclear Instruments and Methods},
  volume = {162},
  number = {1},
  pages = {587--601},
  issn = {0029-554X},
  doi = {10.1016/0029-554X(79)90734-1},
  urldate = {2024-10-26},
  file = {/Users/zain/Zotero/storage/AMTKSS39/0029554X79907341.html}
}

@article{laulheUltrafastFormationCharge2017,
  title = {Ultrafast {{Formation}} of a {{Charge Density Wave State}} in 1 {{T}} - {{TaS}} 2 : {{Observation}} at {{Nanometer Scales Using Time-Resolved X-Ray Diffraction}}},
  shorttitle = {Ultrafast {{Formation}} of a {{Charge Density Wave State}} in 1 {{T}} - {{TaS}} 2},
  author = {Laulh{\'e}, C. and Huber, T. and Lantz, G. and Ferrer, A. and Mariager, S. O. and Gr{\"u}bel, S. and Rittmann, J. and Johnson, J. A. and Esposito, V. and L{\"u}bcke, A. and Huber, L. and Kubli, M. and Savoini, M. and Jacques, V. L. R. and Cario, L. and Corraze, B. and Janod, E. and Ingold, G. and Beaud, P. and Johnson, S. L. and Ravy, S.},
  year = {2017},
  month = jun,
  journal = {Physical Review Letters},
  volume = {118},
  number = {24},
  pages = {247401},
  issn = {0031-9007, 1079-7114},
  doi = {10.1103/PhysRevLett.118.247401},
  urldate = {2024-11-24},
  copyright = {http://link.aps.org/licenses/aps-default-license},
  langid = {english},
  file = {/Users/zain/Zotero/storage/I3JYXG9J/Laulhé et al. - 2017 - Ultrafast Formation of a Charge Density Wave State in 1 T − TaS 2  Observation at Nanometer Scales.pdf}
}

@inproceedings{lehtinenNoise2NoiseLearningImage2018,
  title = {{{Noise2Noise}}: {{Learning Image Restoration}} without {{Clean Data}}},
  shorttitle = {{{Noise2Noise}}},
  booktitle = {Proceedings of the 35th {{International Conference}} on {{Machine Learning}}},
  author = {Lehtinen, Jaakko and Munkberg, Jacob and Hasselgren, Jon and Laine, Samuli and Karras, Tero and Aittala, Miika and Aila, Timo},
  year = {2018},
  month = jul,
  pages = {2965--2974},
  publisher = {PMLR},
  issn = {2640-3498},
  urldate = {2024-08-16},
  abstract = {We apply basic statistical reasoning to signal reconstruction by machine learning - learning to map corrupted observations to clean signals - with a simple and powerful conclusion: it is possible to learn to restore images by only looking at corrupted examples, at performance at and sometimes exceeding training using clean data, without explicit image priors or likelihood models of the corruption. In practice, we show that a single model learns photographic noise removal, denoising synthetic Monte Carlo images, and reconstruction of undersampled MRI scans - all corrupted by different processes - based on noisy data only.},
  langid = {english},
  keywords = {Autoencoder,Deep Learning,Noise2Noise},
  file = {/Users/zain/Documents/Masters/Thesis/papers/JD-26-supp.pdf;/Users/zain/Documents/Masters/Thesis/papers/Lehtinen et al. - 2018 - Noise2Noise Learning Image Restoration without Clean Data.pdf}
}

@article{leitenstorferElectronsBunchQuantum2024,
  title = {Electrons Bunch up in Quantum Light},
  author = {Leitenstorfer, Alfred and Baum, Peter},
  year = {2024},
  month = jun,
  journal = {Nature Physics},
  volume = {20},
  number = {6},
  pages = {890--891},
  publisher = {Nature Publishing Group},
  issn = {1745-2481},
  doi = {10.1038/s41567-024-02473-5},
  urldate = {2024-09-06},
  abstract = {When photons impinge on a material, free electrons can be created by the photoelectric effect. The emitted electron current usually fluctuates with Poisson statistics, but if squeezed quantum light is applied, the electrons bunch up.},
  copyright = {2024 Springer Nature Limited},
  langid = {english},
  keywords = {Nanophotonics and plasmonics,Nonlinear optics,Quantum optics},
  file = {/Users/zain/Zotero/storage/IKZXVJKV/Leitenstorfer and Baum - 2024 - Electrons bunch up in quantum light.pdf}
}

@article{linzhangFSIMFeatureSimilarity2011,
  title = {{{FSIM}}: {{A Feature Similarity Index}} for {{Image Quality Assessment}}},
  shorttitle = {{{FSIM}}},
  author = {{Lin Zhang} and {Lei Zhang} and {Xuanqin Mou} and Zhang, D.},
  year = {2011},
  month = aug,
  journal = {IEEE Transactions on Image Processing},
  volume = {20},
  number = {8},
  pages = {2378--2386},
  issn = {1057-7149, 1941-0042},
  doi = {10.1109/TIP.2011.2109730},
  urldate = {2024-10-25},
  copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
  file = {/Users/zain/Zotero/storage/7CJZ55GK/FSIM_A_Feature_Similarity_Index_for_Image_Quality_Assessment.pdf;/Users/zain/Zotero/storage/H5AB9FPY/Lin Zhang et al. - 2011 - FSIM A Feature Similarity Index for Image Quality Assessment.pdf}
}

@book{loudonQuantumTheoryLight2000,
  title = {The Quantum Theory of Light},
  author = {Loudon, Rodney},
  year = {2000},
  series = {Oxford Science Publications},
  edition = {3rd ed},
  publisher = {Oxford University Press},
  address = {Oxford ; New York},
  isbn = {978-0-19-850177-0 978-0-19-850176-3},
  langid = {english},
  lccn = {QC446.2 .L68 2000},
  keywords = {Quantum optics},
  file = {/Users/zain/Zotero/storage/BA8NXRPN/Loudon - 2000 - The quantum theory of light.pdf}
}

@article{macklinHighorderHarmonicGeneration1993,
  title = {High-Order Harmonic Generation Using Intense Femtosecond Pulses},
  author = {Macklin, J. J. and Kmetec, J. D. and Gordon, C. L.},
  year = {1993},
  month = feb,
  journal = {Physical Review Letters},
  volume = {70},
  number = {6},
  pages = {766--769},
  issn = {0031-9007},
  doi = {10.1103/PhysRevLett.70.766},
  urldate = {2024-10-29},
  copyright = {http://link.aps.org/licenses/aps-default-license},
  langid = {english},
  file = {/Users/zain/Zotero/storage/S9VYCTDP/Macklin et al. - 1993 - High-order harmonic generation using intense femtosecond pulses.pdf}
}

@article{maggionimNonlocalTransformdomainFilter,
  title = {Nonlocal Transform-Domain Filter for Volumetric Data Denoising and Reconstruction},
  author = {{Maggioni M} and {Katkovnik V} and {Egiazarian K} and {Foi A}},
  journal = {PubMed},
  urldate = {2024-10-17},
  abstract = {We present an extension of the BM3D filter to volumetric data. The proposed algorithm, BM4D, implements the grouping and collaborative filtering paradigm, where mutually similar d-dimensional patches are stacked together in a (d+1)-dimensional array and jointly filtered in transform domain. While in {\dots}},
  langid = {english},
  file = {/Users/zain/Zotero/storage/9YDQT7SC/22868570.html}
}

@inproceedings{makinenExactTransformDomainNoise2019,
  title = {Exact {{Transform-Domain Noise Variance}} for {{Collaborative Filtering}} of {{Stationary Correlated Noise}}},
  booktitle = {2019 {{IEEE International Conference}} on {{Image Processing}} ({{ICIP}})},
  author = {Makinen, Ymir and Azzari, Lucio and Foi, Alessandro},
  year = {2019},
  month = sep,
  pages = {185--189},
  publisher = {IEEE},
  address = {Taipei, Taiwan},
  doi = {10.1109/ICIP.2019.8802964},
  urldate = {2024-09-25},
  abstract = {Collaborative filters perform denoising through transformdomain shrinkage of a group of similar blocks extracted from an image. Existing methods for collaborative filtering of stationary correlated noise have all used simple approximations of the transform noise power spectrum adopted from methods which do not employ block grouping. We note the inaccuracies of these approximations and introduce a method for the exact computation and effective approximations of the noise power spectrum. Unlike earlier methods, the calculated noise variances are exact even when noise in one block is correlated with noise in any of the other blocks. We discuss the adoption of the exact noise power spectrum within shrinkage, in similarity testing (block matching), and in aggregation. Extensive experiments support the proposed method over earlier crude approximations used by image denoising filters such as BM3D, demonstrating dramatic improvement in many challenging conditions.},
  copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
  isbn = {978-1-5386-6249-6},
  langid = {english},
  file = {/Users/zain/Zotero/storage/Z8JSPELL/Makinen et al. - 2019 - Exact Transform-Domain Noise Variance for Collaborative Filtering of Stationary Correlated Noise.pdf}
}

@article{makitaloClosedFormApproximationExact2011,
  title = {A {{Closed-Form Approximation}} of the {{Exact Unbiased Inverse}} of the {{Anscombe Variance-Stabilizing Transformation}}},
  author = {Makitalo, Markku and Foi, Alessandro},
  year = {2011},
  month = sep,
  journal = {IEEE Transactions on Image Processing},
  volume = {20},
  number = {9},
  pages = {2697--2698},
  issn = {1941-0042},
  doi = {10.1109/TIP.2011.2121085},
  urldate = {2024-11-06},
  abstract = {We presented an exact unbiased inverse of the Anscombe variance-stabilizing transformation in [M. M{\"a}kitalo and A. Foi, ``Optimal inversion of the Anscombe transformation in low-count Poisson image denoising,'' IEEE Trans. Image Process., vol. 20, no. 1, pp. 99-109, Jan. 2011.] and showed that when applied to Poisson image denoising, the combination of variance stabilization and state-of-the-art Gaussian denoising algorithms is competitive with some of the best Poisson denoising algorithms. We also provided a MATLAB implementation of our method, where the exact unbiased inverse transformation appears in nonanalytical form. Here, we propose a closed-form approximation of the exact unbiased inverse in order to facilitate the use of this inverse. The proposed approximation produces results equivalent to those obtained with the accurate (nonanalytical) exact unbiased inverse, and thus, notably better than one would get with the asymptotically unbiased inverse transformation that is commonly used in applications.},
  keywords = {Approximation algorithms,Approximation methods,Denoising,Image denoising,Imaging,Integrated circuits,Noise,Noise reduction,photon-limited imaging,Poisson noise,variance stabilization},
  file = {/Users/zain/Zotero/storage/HPAS4BE4/Makitalo and Foi - 2011 - A Closed-Form Approximation of the Exact Unbiased Inverse of the Anscombe Variance-Stabilizing Trans.pdf}
}

@article{makitaloOptimalInversionAnscombe2011,
  title = {Optimal {{Inversion}} of the {{Anscombe Transformation}} in {{Low-Count Poisson Image Denoising}}},
  author = {Makitalo, M and Foi, A},
  year = {2011},
  month = jan,
  journal = {IEEE Transactions on Image Processing},
  volume = {20},
  number = {1},
  pages = {99--109},
  issn = {1057-7149},
  doi = {10.1109/TIP.2010.2056693},
  urldate = {2024-09-25},
  copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
  langid = {english},
  file = {/Users/zain/Zotero/storage/EW4UWYGE/Makitalo and Foi - 2011 - Optimal Inversion of the Anscombe Transformation in Low-Count Poisson Image Denoising.pdf}
}

@article{maklarQuantitativeComparisonTimeflight2020,
  title = {A Quantitative Comparison of Time-of-Flight Momentum Microscopes and Hemispherical Analyzers for Time- and Angle-Resolved Photoemission Spectroscopy Experiments},
  author = {Maklar, J. and Dong, S. and Beaulieu, S. and Pincelli, T. and Dendzik, M. and Windsor, Y. W. and Xian, R. P. and Wolf, M. and Ernstorfer, R. and Rettig, L.},
  year = {2020},
  month = dec,
  journal = {Review of Scientific Instruments},
  volume = {91},
  number = {12},
  pages = {123112},
  issn = {0034-6748},
  doi = {10.1063/5.0024493},
  urldate = {2024-10-19},
  abstract = {Time-of-flight-based momentum microscopy has a growing presence in photoemission studies, as it enables parallel energy- and momentum-resolved acquisition of the full photoelectron distribution. Here, we report table-top extreme ultraviolet time- and angle-resolved photoemission spectroscopy (trARPES) featuring both a hemispherical analyzer and a momentum microscope within the same setup. We present a systematic comparison of the two detection schemes and quantify experimentally relevant parameters, including pump- and probe-induced space-charge effects, detection efficiency, photoelectron count rates, and depth of focus. We highlight the advantages and limitations of both instruments based on exemplary trARPES measurements of bulk WSe2. Our analysis demonstrates the complementary nature of the two spectrometers for time-resolved ARPES experiments. Their combination in a single experimental apparatus allows us to address a broad range of scientific questions with trARPES.},
  file = {/Users/zain/Zotero/storage/GTHF2ACA/Maklar et al. - 2020 - A quantitative comparison of time-of-flight momentum microscopes and hemispherical analyzers for tim.pdf;/Users/zain/Zotero/storage/WEHSUEPI/A-quantitative-comparison-of-time-of-flight.html}
}

@misc{maklarTimeresolvedARPESRAW2022,
  title = {Time-Resolved {{ARPES RAW}} Data of Bulk {{WSe2}} for a Quantitative Comparison of Time-of-Flight Momentum Microscopes and Hemispherical Analyzers: {{RAW MM}} Data},
  shorttitle = {Time-Resolved {{ARPES RAW}} Data of Bulk {{WSe2}} for a Quantitative Comparison of Time-of-Flight Momentum Microscopes and Hemispherical Analyzers},
  author = {Maklar, Julian and Dong, Shuo and Beaulieu, Samuel and Pincelli, Tommaso and Dendzik, Maciej and Windsor, Y. William and Xian, R. Patrick and Wolf, Martin and Ernstorfer, Ralph and Rettig, Laurenz},
  year = {2022},
  month = mar,
  publisher = {Zenodo},
  doi = {10.5281/ZENODO.6369727},
  urldate = {2024-10-25},
  abstract = {Time- and angle-resolved photoemission spectroscopy data of bulk WSe2 using a laser-based XUV source and a time-of-flight momentum microscope analyzer. The dataset here comprises part of the machine RAW data used to construct the processed data stored in the datasets at https://doi.org/10.5281/zenodo.4067968 Analysis scripts and conversion tools into NeXus format can be found at https://github.com/nomad-coe/nomad-parser-nexus/tree/master/tests/data/tools/dataconverter/readers/mpes},
  copyright = {Creative Commons Attribution 4.0 International, Open Access},
  keywords = {ARPES,Momentum Microscopy,Photoemission,Pump-probe,Time-resolved,TMDC,Tungstendiselenide,WSe2}
}

@article{mandelFluctuationsPhotonBeams1958,
  title = {Fluctuations of {{Photon Beams}} and Their {{Correlations}}},
  author = {Mandel, L.},
  year = {1958},
  month = dec,
  journal = {Proceedings of the Physical Society},
  volume = {72},
  number = {6},
  pages = {1037},
  issn = {0370-1328},
  doi = {10.1088/0370-1328/72/6/312},
  urldate = {2024-09-25},
  abstract = {The distribution of counts from a photoelectric detector illuminated by light of bandwidth {$\Delta\nu$}0 is analysed by associating the photons with Gaussian random waves. This is shown to lead to a full statistical description of the counts. It is shown that the number nT in a time interval T {$<<$} 1/{$\Delta\nu$}0 obeys pure Bose-Einstein statistics, and that the fluctuations in longer intervals T {$>>$} 1/{$\Delta\nu$}0 are simply the density fluctuations of a boson assembly in a phase space of {$\Delta\nu$}0T cells. The correlation coefficient {$\rho$} of the fluctuations of counts from two detectors illuminated by partially coherent beams is found to be proportional to the local time average of the square of the coherence function {$\langle\gamma$}122{$\rangle$}. The correlation is shown to depend on the degeneracy of the beams in such a way that {$\rho$} {$\rightarrow$} 2{$\langle\gamma$}122{$\rangle$} for highly degenerate beams. The results are all consistent with those obtained by Hanbury Brown and Twiss in 1957.},
  langid = {english},
  file = {/Users/zain/Zotero/storage/NTJTJTAC/Mandel - 1958 - Fluctuations of Photon Beams and their Correlations.pdf}
}

@article{mandelFluctuationsPhotonBeams1959,
  title = {Fluctuations of {{Photon Beams}}: {{The Distribution}} of the {{Photo-Electrons}}},
  shorttitle = {Fluctuations of {{Photon Beams}}},
  author = {Mandel, L.},
  year = {1959},
  month = sep,
  journal = {Proceedings of the Physical Society},
  volume = {74},
  number = {3},
  pages = {233},
  issn = {0370-1328},
  doi = {10.1088/0370-1328/74/3/301},
  urldate = {2024-09-25},
  abstract = {The probability distribution p(n, T) of the number of counts n from a photoelectric detector illuminated by coherent light for a time T is studied, by associating photons stochastically with Gaussian random waves. The cumulants of the distribution are derived and it is shown to be of the expected form for a boson assembly in a limited volume of phase space. The distribution depends strongly on the degeneracy of the light beam. It approaches the Poisson form for classical particles at low degeneracies and the distribution characteristic of classical waves at high degeneracies. The analysis leads, incidentally, to an expression for the extent of the unit cell of phase space in the direction of the beam. It is argued that this should be adopted as the measure of coherence length.},
  langid = {english},
  file = {/Users/zain/Zotero/storage/98J4XRKY/Mandel - 1959 - Fluctuations of Photon Beams The Distribution of the Photo-Electrons.pdf}
}

@misc{marketingartadminsurfaceSegmentedAnodeDelayline,
  title = {Segmented {{Anode Delayline Detectors}}},
  author = {{MarketingArtAdminSurface}},
  journal = {Surface Concept GmbH - Mainz},
  urldate = {2024-10-20},
  abstract = {Segmented Anode Delayline Detectors    Microchannel plate based detector packages with a segmented meander structured delayline anode, especially designed for position and time resolved multihit detection of electrons, ions and photons with sub-ns time resolution and {\textmu}m spatial resolution by a parallel and independent readout of each},
  langid = {british},
  file = {/Users/zain/Zotero/storage/B4QU99N2/segmented-anode-delayline-detectors.html}
}

@article{martinsMonochromatorBeamlineFLASH2006,
  title = {Monochromator Beamline for {{FLASH}}},
  author = {Martins, M. and Wellh{\"o}fer, M. and Hoeft, J. T. and others},
  year = {2006},
  month = nov,
  journal = {Review of Scientific Instruments},
  volume = {77},
  number = {11},
  pages = {115108},
  publisher = {AIP Publishing},
  doi = {10.1063/1.2364148}
}

@misc{MCPDetectors,
  title = {{{MCP}} Detectors},
  urldate = {2024-11-01},
  howpublished = {https://www.atom.uni-frankfurt.de/research/10\_COLTRIMS/20\_MCP-detectors/}
}

@incollection{mehtaVIIITheoryPhotoelectron1970,
  title = {{{VIII Theory}} of {{Photoelectron Counting}}},
  booktitle = {Progress in {{Optics}}},
  author = {Mehta, C.L.},
  year = {1970},
  volume = {8},
  pages = {373--440},
  publisher = {Elsevier},
  doi = {10.1016/S0079-6638(08)70193-8},
  urldate = {2024-09-25},
  isbn = {978-0-444-10020-7},
  langid = {english},
  file = {/Users/zain/Zotero/storage/E8CESC87/Mehta - 1970 - VIII Theory of Photoelectron Counting.pdf}
}

@article{meierFewelectronCorrelationsUltrafast2023a,
  title = {Few-Electron Correlations after Ultrafast Photoemission from Nanometric Needle Tips},
  author = {Meier, Stefan and Heimerl, Jonas and Hommelhoff, Peter},
  year = {2023},
  month = oct,
  journal = {Nature Physics},
  volume = {19},
  number = {10},
  pages = {1402--1409},
  publisher = {Nature Publishing Group},
  issn = {1745-2481},
  doi = {10.1038/s41567-023-02059-7},
  urldate = {2024-08-28},
  abstract = {Free electrons are essential in such diverse applications as electron microscopes, accelerators and photoemission spectroscopy. However, the space charge effects of many electrons are often a problem and, when confined to extremely small space--time dimensions, even two electrons can interact strongly. Here we demonstrate that the resulting Coulomb repulsion can be highly advantageous, as it leads to strong electron--electron correlations. We show that femtosecond laser-emitted electrons from nanometric needle tips are highly anticorrelated in terms of energy because of dynamic Coulomb repulsion, with a visibility of 56\%. We extract a mean energy splitting of 3.3\,eV and a correlation decay time of 82\,fs. The energy-filtered electrons display a sub-Poissonian number distribution with a second-order correlation function as small as g(2)\,=\,0.34, implying that shot-noise-reduced pulsed electron beams can be realized by simple energy filtering. We also reach the strong-field regime of laser-driven electron emission and gain insights into how the electron correlations of the different electron classes (direct or rescattered) are influenced by the strong laser fields.},
  copyright = {2023 The Author(s), under exclusive licence to Springer Nature Limited},
  langid = {english},
  keywords = {Matter waves and particle beams,Nanophotonics and plasmonics},
  file = {/Users/zain/Zotero/storage/YXAFHT9R/Meier et al. - 2023 - Few-electron correlations after ultrafast photoemission from nanometric needle tips.pdf}
}

@misc{MultidimensionalPhotoemissionSpectroscopy,
  title = {Multidimensional Photoemission Spectroscopy---the Space-Charge Limit - {{IOPscience}}},
  urldate = {2024-10-22},
  howpublished = {https://iopscience.iop.org/article/10.1088/1367-2630/aaa262},
  file = {/Users/zain/Zotero/storage/UTSMHNSI/aaa262.html}
}

@book{novotnyPrinciplesNanooptics2012d,
  title = {Principles of Nano-Optics},
  author = {Novotny, Lukas and Hecht, Bert},
  year = {2012},
  edition = {2nd ed},
  publisher = {Cambridge University Press},
  address = {Cambridge},
  isbn = {978-1-107-00546-4},
  lccn = {TA418.9.N35 N68 2012},
  keywords = {Nanophotonics,Nanostructured materials,Near-field microscopy,Photonics,Quantum optics,SCIENCE / Optics},
  file = {/Users/zain/Zotero/storage/84GXHX4Z/Novotny and Hecht - 2012 - Principles of nano-optics.pdf}
}

@article{oelsnerMicrospectroscopyImagingUsing2001,
  title = {Microspectroscopy and Imaging Using a Delay Line Detector in Time-of-Flight Photoemission Microscopy},
  author = {Oelsner, A. and Schmidt, O. and Schicketanz, M. and Klais, M. and Sch{\"o}nhense, G. and Mergel, V. and Jagutzki, O. and {Schmidt-B{\"o}cking}, H.},
  year = {2001},
  month = oct,
  journal = {Review of Scientific Instruments},
  volume = {72},
  number = {10},
  pages = {3968--3974},
  issn = {0034-6748},
  doi = {10.1063/1.1405781},
  urldate = {2024-10-29},
  abstract = {A method for microspectroscopy and energy-selective imaging using a special photoemission electron microscope (PEEM) is presented. A modified commercial PEEM was combined with a delay line device as x, y, t detector serving as the basic arrangement for spectromicroscopy. One can measure the time of flight of the electrons passing a drift section in order to analyze the energy distribution of photoelectrons in PEEM. The time of flight is referenced to the time structure of the synchrotron radiation from an electron storage ring. At electron kinetic energies of less than 20 eV within the drift region a spatial resolution of about 100 nm has been obtained. Fast counting electronics (instead of a camera) delivers an image for real-time monitoring on an oscilloscope screen or for image acquisition by a computer. A time resolution of about 500 ps has been obtained with the potential of further improvement. The spatial resolution of the delay line detector is about 50 {$\mu$}m in the image plane corresponding to 1000 pixels in the image diagonal. Direct photoemission from the W-4f core level of a W(110) single-crystal sample was observed at several photon energies. The W-4f fine-structure splitting of 2.3 eV could be well resolved at a pass energy around 40 eV through the drift region.},
  file = {/Users/zain/Zotero/storage/IMM4JIZC/Oelsner et al. - 2001 - Microspectroscopy and imaging using a delay line detector in time-of-flight photoemission microscopy.pdf;/Users/zain/Zotero/storage/J8BUV8ZN/Microspectroscopy-and-imaging-using-a-delay-line.html}
}

@article{oelsnerTimeEnergyResolved2010,
  title = {Time- and Energy Resolved Photoemission Electron Microscopy-Imaging of Photoelectron Time-of-Flight Analysis by Means of Pulsed Excitations},
  author = {Oelsner, Andreas and Rohmer, Martin and Schneider, Christian and Bayer, Daniela and Sch{\"o}nhense, Gerd and Aeschlimann, Martin},
  year = {2010},
  month = may,
  journal = {Journal of Electron Spectroscopy and Related Phenomena},
  volume = {178--179},
  pages = {317--330},
  issn = {03682048},
  doi = {10.1016/j.elspec.2009.10.008},
  urldate = {2024-10-29},
  abstract = {The present work enlightens the developments in time- and energy resolved photoemission electron microscopy over the past few years. We describe basic principles of the technique and demonstrate different applications. An energy- and time-filtering photoemission electron microscopy (PEEM) for realtime spectroscopic imaging can be realized either by a retarding field or hemispherical energy analyzer or by using time-of-flight optics with a delay line detector. The latter method has the advantage of no data loss at all as all randomly incoming particles are measured not only by position but also by time. This is of particular interest for pump--probe experiments in the femtosecond and attosecond time scale where space charge processes drastically limit the maximum number of photoemitted electrons per laser pulse. This work focuses particularly on time-of-flight analysis using a novel delay line detector. Time and energy resolved PEEM instruments with delay line detectors enable 4D imaging (x, y,  t, EKin) on a true counting basis. This allows a broad range of applications from real-time observation of dynamic phenomena at surfaces to fs time-of-flight spectro-microscopy and even aberration correction.},
  copyright = {https://www.elsevier.com/tdm/userlicense/1.0/},
  langid = {english},
  file = {/Users/zain/Zotero/storage/NTZZL6XU/Oelsner et al. - 2010 - Time- and energy resolved photoemission electron microscopy-imaging of photoelectron time-of-flight.pdf}
}

@book{oppenheimSignalsSystemsInference2017,
  title = {Signals, Systems \& Inference},
  author = {Oppenheim, Alan V. and Verghese, George C.},
  year = {2017},
  edition = {Global edition},
  publisher = {Pearson},
  address = {Harlow},
  isbn = {978-1-292-15620-0},
  langid = {english},
  annotation = {OCLC: 981472137},
  file = {/Users/zain/Zotero/storage/C2DJLZTE/Oppenheim and Verghese - Signals, Systems & Inference.pdf;/Users/zain/Zotero/storage/XAUIQ76E/f135b328c7448bf21c4939ea9ff8f8fb_MIT6_011S10_chap11.pdf}
}

@incollection{paschottaMicrochannelPlatesEncyclopedia2019,
  title = {Microchannel {{Plates}} - an Encyclopedia Article},
  booktitle = {{{RP Photonics Encyclopedia}}},
  author = {Paschotta, R.},
  year = {2019},
  publisher = {RP Photonics AG},
  doi = {10.61835/u9s},
  urldate = {2024-10-26},
  langid = {english}
}

@inproceedings{paszkePyTorchImperativeStyle2019,
  title = {{{PyTorch}}: {{An Imperative Style}}, {{High-Performance Deep Learning Library}}},
  shorttitle = {{{PyTorch}}},
  booktitle = {Advances in {{Neural Information Processing Systems}}},
  author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
  year = {2019},
  volume = {32},
  publisher = {Curran Associates, Inc.},
  urldate = {2024-10-11},
  abstract = {Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it was designed from first principles to support an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several commonly used benchmarks.},
  file = {/Users/zain/Zotero/storage/TWJYPYR5/Paszke et al. - 2019 - PyTorch An Imperative Style, High-Performance Deep Learning Library.pdf}
}

@article{petekFemtosecondTimeresolvedTwophoton1997,
  title = {Femtosecond Time-Resolved Two-Photon Photoemission Studies of Electron Dynamics in Metals},
  author = {Petek, H. and Ogawa, S.},
  year = {1997},
  month = dec,
  journal = {Progress in Surface Science},
  volume = {56},
  number = {4},
  pages = {239--310},
  issn = {00796816},
  doi = {10.1016/S0079-6816(98)00002-1},
  urldate = {2024-11-24},
  copyright = {https://www.elsevier.com/tdm/userlicense/1.0/},
  langid = {english}
}

@book{PhotoemissionSolids11978,
  title = {Photoemission in Solids. 1: {{General}} Principles},
  shorttitle = {Photoemission in Solids. 1},
  year = {1978},
  series = {Topics in Applied Physics},
  number = {26},
  publisher = {Springer},
  address = {Berlin Heidelberg New York},
  isbn = {978-3-540-08685-7 978-0-387-08685-9},
  langid = {english}
}

@unpublished{PhotonStatisticsLab,
  title = {Photon {{Statistics Lab Manual}}},
  keywords = {Photon,Physics,Poisson,Statistics},
  file = {/Users/zain/Documents/Masters/Thesis/books/photon_stats_lab_manual.pdf}
}

@book{poissonRecherchesProbabiliteJugements1837,
  title = {{Recherches sur la probabilit{\'e} des jugements en mati{\`e}re criminelle et en mati{\`e}re civile: pr{\'e}c{\'e}d{\'e}es des r{\`e}gles g{\'e}n{\'e}rales du calcul des probabilit{\'e}s}},
  shorttitle = {{Recherches sur la probabilit{\'e} des jugements en mati{\`e}re criminelle et en mati{\`e}re civile}},
  author = {Poisson, Sim{\'e}on-Denis},
  year = {1837},
  publisher = {Bachelier},
  googlebooks = {uB8OAAAAQAAJ},
  langid = {french}
}

@article{restrepoDenoisingFeatureExtraction2022,
  title = {Denoising and Feature Extraction in Photoemission Spectra with Variational Auto-Encoder Neural Networks},
  author = {Restrepo, Francisco and Zhao, Junjing and Chatterjee, Utpal},
  year = {2022},
  month = jun,
  journal = {Review of Scientific Instruments},
  volume = {93},
  number = {6},
  pages = {065106},
  issn = {0034-6748, 1089-7623},
  doi = {10.1063/5.0090051},
  urldate = {2024-08-19},
  abstract = {In recent years, distinct machine learning (ML) models have been separately used for feature extraction and noise reduction from energy--momentum dispersion intensity maps obtained from raw angle-resolved photoemission spectroscopy (ARPES) data. In this work, we employ a shallow variational auto-encoder neural network to demonstrate the prospect of using ML for both denoising of as well as feature extraction from ARPES dispersion maps.},
  langid = {english},
  keywords = {ARPES,Autoencoder,Deep Learning},
  file = {/Users/zain/Documents/Masters/Thesis/papers/JD-21.pdf}
}

@misc{ronnebergerUNetConvolutionalNetworks,
  title = {U-{{Net}}: {{Convolutional Networks}} for {{Biomedical Image Segmentation}}},
  shorttitle = {U-{{Net}}},
  author = {Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
  journal = {arXiv.org},
  urldate = {2024-10-09},
  abstract = {There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .},
  howpublished = {https://arxiv.org/abs/1505.04597v1},
  langid = {english},
  file = {/Users/zain/Zotero/storage/55IY97FC/Ronneberger et al. - U-Net Convolutional Networks for Biomedical Image Segmentation.pdf}
}

@article{rosenblattPerceptronProbabilisticModel1958,
  title = {The Perceptron: {{A}} Probabilistic Model for Information Storage and Organization in the Brain},
  shorttitle = {The Perceptron},
  author = {Rosenblatt, F.},
  year = {1958},
  journal = {Psychological Review},
  volume = {65},
  number = {6},
  pages = {386--408},
  publisher = {American Psychological Association},
  address = {US},
  issn = {1939-1471},
  doi = {10.1037/h0042519},
  abstract = {To answer the questions of how information about the physical world is sensed, in what form is information remembered, and how does information retained in memory influence recognition and behavior, a theory is developed for a hypothetical nervous system called a perceptron. The theory serves as a bridge between biophysics and psychology. It is possible to predict learning curves from neurological variables and vice versa. The quantitative statistical approach is fruitful in the understanding of the organization of cognitive systems. 18 references. (PsycINFO Database Record (c) 2016 APA, all rights reserved)},
  keywords = {Brain,Cognition,Memory,Nervous System},
  file = {/Users/zain/Zotero/storage/UUWTF8Z3/1959-09865-001.html}
}

@misc{Rozet_PIQA_PyTorch_Image_2020,
  title = {{{PIQA}}: {{PyTorch}} Image Quality Assessement},
  author = {Rozet, Fran{\c c}ois},
  year = {2020},
  month = dec,
  doi = {10.5281/zenodo.7821598},
  copyright = {MIT}
}

@article{sagewerkSurfaceConceptDLD,
  title = {Surface {{Concept DLD}}},
  author = {S{\"a}gewerk, Am},
  langid = {english},
  keywords = {DLD},
  file = {/Users/zain/Zotero/storage/4SZYPXVM/Sägewerk - email support@surface-concept.de www.surface-concept.de.pdf}
}

@book{sakuraiModernQuantumMechanics2020,
  title = {Modern {{Quantum Mechanics}}},
  author = {Sakurai, J. J. and Napolitano, Jim},
  year = {2020},
  month = sep,
  edition = {3},
  publisher = {Cambridge University Press},
  doi = {10.1017/9781108587280},
  urldate = {2024-10-22},
  abstract = {Modern Quantum Mechanics is a classic graduate level textbook, covering the main concepts from quantum mechanics in a clear, organized and engaging manner. The original author, J. J. Sakurai, was a renowned particle theorist. This third edition, revised by Jim Napolitano, introduces topics that extend its value into the twenty-first century, such as modern mathematical techniques for advanced quantum mechanical calculations, while at the same time retaining fundamental topics such as neutron interferometer experiments, Feynman path integrals, correlation measurements, and Bell's inequalities. A solutions manual is available.},
  copyright = {https://www.cambridge.org/core/terms},
  isbn = {978-1-108-58728-0 978-1-108-47322-4},
  langid = {english},
  file = {/Users/zain/Zotero/storage/ADQCN4TZ/Sakurai and Napolitano - 2020 - Modern Quantum Mechanics.pdf}
}

@article{saldinPhysicsFreeElectron1995,
  title = {The Physics of Free Electron Lasers. {{An}} Introduction},
  author = {Saldin, E. L. and Schneidmiller, E. A. and Yurkov, M. V.},
  year = {1995},
  month = sep,
  journal = {Physics Reports},
  volume = {260},
  number = {4},
  pages = {187--327},
  issn = {0370-1573},
  doi = {10.1016/0370-1573(95)00004-Z},
  urldate = {2024-09-03},
  abstract = {The present paper contains a systematic exposure of basic principles of free electron laser physics. FEL models are discussed wherein space charge fields, energy spread of the electrons in the beam and diffraction effects are taken into account. We present a detailed study of the one-dimensional theory of a FEL amplifier, FEL oscillator and FEL oscillator with multicomponent undulator. We present also an introduction to the analysis of the diffraction effects using the FEL amplifier model. When exposing linear theory we use analytical techniques and the investigation of nonlinear modes in the operation of FEL devices is performed using the results of numerical simulations. The results of the linear theory serve as a primary standard to check the accuracy of numerical simulations. We perform a thorough analysis of the saturation effects in the FEL devices and of the methods to increase the FEL efficiency. A theoretical study is performed with a wide use of similarity techniques, so the obtained results are simultaneously of high degree of generality and completely specified. Numerous universal graphs illustrating various modes of the FEL operation together with the reduced design formulae can help the reader to find FEL characteristics using simple dimensional analysis only. It may be useful also for FEL designers, especially at the design stage of an experiment.},
  file = {/Users/zain/Zotero/storage/6AXZWJIX/Saldin et al. - 1995 - The physics of free electron lasers. An introduction.pdf;/Users/zain/Zotero/storage/7V76UR7T/037015739500004Z.html}
}

@article{saldinStatisticalPropertiesRadiation1998,
  title = {Statistical Properties of Radiation from {{VUV}} and {{X-ray}} Free Electron Laser},
  author = {Saldin, E. L. and Schneidmiller, E. A. and Yurkov, M. V.},
  year = {1998},
  month = mar,
  journal = {Optics Communications},
  volume = {148},
  number = {4},
  pages = {383--403},
  issn = {0030-4018},
  doi = {10.1016/S0030-4018(97)00670-6},
  urldate = {2024-09-03},
  abstract = {The paper presents a comprehensive analysis of the statistical properties of the radiation from a self-amplified spontaneous emission (SASE) free electron laser operating in linear and nonlinear mode. The investigation has been performed in a one-dimensional approximation assuming the electron pulse length to be much larger than a coherence length of the radiation. The following statistical properties of the SASE FEL radiation have been studied in detail: time and spectral field correlations, distribution of the fluctuations of the instantaneous radiation power, distribution of the energy in the electron bunch, distribution of the radiation energy after the monochromator installed at the FEL amplifier exit and radiation spectrum. The linear high gain limit is studied analytically. It is shown that the radiation from a SASE FEL operating in the linear regime possesses all the features corresponding to completely chaotic polarized radiation. A detailed study of statistical properties of the radiation from a SASE FEL operating in linear and nonlinear regime has been performed by means of time-dependent simulation codes. All numerical results presented in the paper have been calculated for the 70 nm SASE FEL at the TESLA Test Facility being under construction at DESY.},
  keywords = {Coherence,Free-electron laser,SASE,X-ray laser},
  file = {/Users/zain/Zotero/storage/4MDEENUE/Saldin et al. - 1998 - Statistical properties of radiation from VUV and X-ray free electron laser.pdf;/Users/zain/Zotero/storage/VW9QBJP7/S0030401897006706.html}
}

@book{salehPhotoelectronStatistics1978,
  title = {Photoelectron {{Statistics}}},
  author = {Saleh, Bahaa},
  editor = {MacAdam, David L.},
  year = {1978},
  series = {Springer {{Series}} in {{Optical Sciences}}},
  volume = {6},
  publisher = {Springer Berlin Heidelberg},
  address = {Berlin, Heidelberg},
  doi = {10.1007/978-3-540-37311-7},
  urldate = {2024-09-24},
  copyright = {http://www.springer.com/tdm},
  isbn = {978-3-662-13483-2 978-3-540-37311-7},
  file = {/Users/zain/Zotero/storage/LP4SUXQJ/Saleh - 1978 - Photoelectron Statistics.pdf}
}

@book{schmuserFreeElectronLasersUltraviolet2014,
  title = {Free-{{Electron Lasers}} in the {{Ultraviolet}} and {{X-Ray Regime}}},
  author = {Schm{\"u}ser, Peter and Dohlus, Martin and Rossbach, J{\"o}rg and Behrens, Christopher},
  year = {2014},
  series = {Springer Tracts in Modern Physics},
  volume = {258},
  publisher = {Springer International Publishing},
  isbn = {978-3-319-04081-3},
  file = {/Users/zain/Zotero/storage/D6UYX9B7/Schmüser et al. - 2014 - Free-Electron Lasers in the Ultraviolet and X-Ray Regime.pdf}
}

@article{schonhenseMultidimensionalPhotoemissionSpectroscopy2018,
  title = {Multidimensional Photoemission Spectroscopy---the Space-Charge Limit},
  author = {Sch{\"o}nhense, B. and Medjanik, K. and Fedchenko, O. and Chernov, S. and Ellguth, M. and Vasilyev, D. and Oelsner, A. and Viefhaus, J. and Kutnyakhov, D. and Wurth, W. and Elmers, H. J. and Sch{\"o}nhense, G.},
  year = {2018},
  month = mar,
  journal = {New Journal of Physics},
  volume = {20},
  number = {3},
  pages = {033004},
  publisher = {IOP Publishing},
  issn = {1367-2630},
  doi = {10.1088/1367-2630/aaa262},
  urldate = {2024-10-22},
  abstract = {Photoelectron spectroscopy, especially at pulsed sources, is ultimately limited by the Coulomb interaction in the electron cloud, changing energy and angular distribution of the photoelectrons. A detailed understanding of this phenomenon is crucial for future pump--probe photoemission studies at (x-ray) free electron lasers and high-harmonic photon sources. Measurements have been performed for Ir(111) at h{$\nu$} = 1000 eV with photon flux densities between {$\sim$}102 and 104 photons per pulse and {$\mu$}m2 (beamline P04/PETRA III, DESY Hamburg), revealing space-charge induced energy shifts of up to 10 eV. In order to correct the essential part of the energy shift and restore the electron distributions close to the Fermi energy, we developed a semi-analytical theory for the space-charge effect in cathode-lens instruments (momentum microscopes, photoemission electron microscopes). The theory predicts a Lorentzian profile of energy isosurfaces and allows us to quantify the charge cloud from measured energy profiles. The correction is essential for the determination of the Fermi surface, as we demonstrate by means of `k-space movies' for the prototypical high-Z material tungsten. In an energy interval of about 1 eV below the Fermi edge, the bandstructure can be restored up to substantial shifts of {$\sim$}7 eV. Scattered photoelectrons strongly enhance the inelastic background in the region several eV below EF, proving that the majority of scattering events involves a slow electron. The correction yields a gain of two orders of magnitude in usable intensity compared with the uncorrected case (assuming a tolerable shift of 250 meV). The results are particularly important for future experiments at SASE-type free electron lasers, since the correction also works for strongly fluctuating (but known) pulse intensities.},
  langid = {english},
  file = {/Users/zain/Zotero/storage/IA2IGV2F/Schönhense et al. - 2018 - Multidimensional photoemission spectroscopy—the space-charge limit.pdf}
}

@article{schonhenseSpaceTimeSpinresolved2015,
  title = {Space-, Time- and Spin-Resolved Photoemission},
  author = {Sch{\"o}nhense, Gerd and Medjanik, Katerina and Elmers, Hans-Joachim},
  year = {2015},
  month = apr,
  journal = {Journal of Electron Spectroscopy and Related Phenomena},
  series = {Special {{Anniversary Issue}}: {{Volume}} 200},
  volume = {200},
  pages = {94--118},
  issn = {0368-2048},
  doi = {10.1016/j.elspec.2015.05.016},
  urldate = {2024-10-30},
  abstract = {This article reviews photoemission experiments that simultaneously resolve at least two of the following degrees of freedom: space (real and momentum space), time (intrinsic time scale of a fast experiment or time-of-flight) and spin. In the spatiotemporal domain, imaging of fast processes by PEEM gives direct insight into plasmon dynamics or magnetization processes. In the category real space \& spin the novel concept of imaging spin filters is discussed. In the time \& spin chapter we address time-of-flight spin detectors and ultrafast spin processes that are accessible by pump-and-probe techniques. A main part of the paper is devoted to the resolution of momentum-space \& time. This is implemented in form of the time-of-flight momentum microscope, a very recent development of which the first instrument has been in operation since 2014. In an extended outlook chapter, the potential of new developments and of a novel, highly parallelized delay-line type electron detector will be discussed. The combination of all three degrees of freedom k-space, time \& spin is emerging through the combination of the ToF momentum microscope with imaging spin filter.},
  keywords = {Imaging,PEEM,Photoemission,Spin filter,Spin resolved,Time resolved,ToF momentum microscope},
  file = {/Users/zain/Zotero/storage/UFZMTHEM/Schönhense et al. - 2015 - Space-, time- and spin-resolved photoemission.pdf;/Users/zain/Zotero/storage/982HRUGV/S0368204815001243.html}
}

@article{schroederPhotonStatisticsSASE2002,
  title = {Photon Statistics of the {{SASE FEL}}},
  author = {Schroeder, C. B.},
  year = {2002},
  month = may,
  journal = {Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment},
  series = {Proceedings of the 23rd {{International Free Electron Laser Confere}} Nce and 8th {{FEL Users Workshop}}},
  volume = {483},
  number = {1},
  pages = {499--503},
  issn = {0168-9002},
  doi = {10.1016/S0168-9002(02)00369-8},
  urldate = {2024-08-29},
  abstract = {The photon statistics of the free-electron laser radiation are examined. The photon number moments are calculated for the multimode self-amplified spontaneous emission including the effects of the Gaussian free-electron laser spectral distribution. It is shown that, in the parameter regime where the pulse duration is of the order of the coherence time, the expectation value of the photon number moments deviate from those predicted by a negative-binomial probability distribution.},
  keywords = {Free-electron laser,Photon statistics,SASE},
  file = {/Users/zain/Zotero/storage/JB4LZVSQ/Schroeder - 2002 - Photon statistics of the SASE FEL.pdf;/Users/zain/Zotero/storage/WGASHY9K/S0168900202003698.html}
}

@book{scullyQuantumOptics1997,
  title = {Quantum Optics},
  author = {Scully, Marlan O. and Zubairy, Muhammad Suhail},
  year = {1997},
  publisher = {Cambridge University Press},
  address = {Cambridge New York Melbourne Madrid Cape Town Singapore S{\~a}oPaulo Delhi Dubai Tokyo Mexiko City},
  isbn = {978-0-511-81399-3 978-0-521-43458-4 978-0-521-43595-6},
  langid = {english},
  file = {/Users/zain/Zotero/storage/2UYIGHIL/Scully and Zubairy - 1997 - Quantum optics.pdf}
}

@article{seresSourceCoherentKiloelectronvolt2005,
  title = {Source of Coherent Kiloelectronvolt {{X-rays}}},
  author = {Seres, J. and Seres, E. and Verhoef, A. J. and Tempea, G. and Streli, C. and Wobrauschek, P. and Yakovlev, V. and Scrinzi, A. and Spielmann, C. and Krausz, F.},
  year = {2005},
  month = feb,
  journal = {Nature},
  volume = {433},
  number = {7026},
  pages = {596--596},
  issn = {0028-0836, 1476-4687},
  doi = {10.1038/433596a},
  urldate = {2024-10-29},
  copyright = {http://www.springer.com/tdm},
  langid = {english},
  file = {/Users/zain/Zotero/storage/CLP7APBG/Seres et al. - 2005 - Source of coherent kiloelectronvolt X-rays.pdf}
}

@book{shalev-shwartzUnderstandingMachineLearning2014a,
  title = {Understanding {{Machine Learning}}: {{From Theory}} to {{Algorithms}}},
  shorttitle = {Understanding {{Machine Learning}}},
  author = {{Shalev-Shwartz}, Shai and {Ben-David}, Shai},
  year = {2014},
  month = may,
  edition = {1},
  publisher = {Cambridge University Press},
  doi = {10.1017/CBO9781107298019},
  urldate = {2024-10-09},
  abstract = {Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.},
  copyright = {https://www.cambridge.org/core/terms},
  isbn = {978-1-107-05713-5 978-1-107-29801-9},
  langid = {english},
  file = {/Users/zain/Zotero/storage/FRLDIG5G/Shalev-Shwartz and Ben-David - 2014 - Understanding Machine Learning From Theory to Algorithms.pdf}
}

@article{shokeenRealtimeObservationNonequilibrium2024,
  title = {Real-Time Observation of Non-Equilibrium Phonon-Electron Energy and Angular Momentum Flow in Laser-Heated Nickel},
  author = {Shokeen, Vishal and Heber, Michael and Kutnyakhov, Dmytro and Wang, Xiaocui and Yaroslavtsev, Alexander and Maldonado, Pablo and Berritta, Marco and Wind, Nils and Wenthaus, Lukas and Pressacco, Federico and Min, Chul-Hee and Nissen, Matz and Mahatha, Sanjoy K. and Dziarzhytski, Siarhei and Oppeneer, Peter M. and Rossnagel, Kai and Elmers, Hans-Joachim and Sch{\"o}nhense, Gerd and D{\"u}rr, Hermann A.},
  year = {2024},
  month = feb,
  journal = {Science Advances},
  volume = {10},
  number = {5},
  pages = {eadj2407},
  issn = {2375-2548},
  doi = {10.1126/sciadv.adj2407},
  urldate = {2024-10-25},
  abstract = {Identifying the microscopic nature of non-equilibrium energy transfer mechanisms among electronic, spin, and lattice degrees of freedom is central to understanding ultrafast phenomena such as manipulating magnetism on the femtosecond timescale. Here, we use time- and angle-resolved photoemission spectroscopy to go beyond the often-used ensemble-averaged view of non-equilibrium dynamics in terms of quasiparticle temperature evolutions. We show for ferromagnetic Ni that the non-equilibrium electron and spin dynamics display pronounced variations with electron momentum, whereas the magnetic exchange interaction remains isotropic. This highlights the influence of lattice-mediated scattering processes and opens a pathway toward unraveling the still elusive microscopic mechanism of spin-lattice angular momentum transfer.           ,              Non-equilibrium phonon-electron scattering leads to momentum-dependent transient electron and spin populations in Ni.},
  langid = {english},
  file = {/Users/zain/Zotero/storage/DTZ56VKG/Shokeen et al. - 2024 - Real-time observation of non-equilibrium phonon-electron energy and angular momentum flow in laser-h.pdf}
}

@article{sjodinUltrafastCarrierDynamics1998,
  title = {Ultrafast {{Carrier Dynamics}} in {{Silicon}}: {{A Two-Color Transient Reflection Grating Study}} on a ( 111 ) {{Surface}}},
  shorttitle = {Ultrafast {{Carrier Dynamics}} in {{Silicon}}},
  author = {Sjodin, Theodore and Petek, Hrvoje and Dai, Hai-Lung},
  year = {1998},
  month = dec,
  journal = {Physical Review Letters},
  volume = {81},
  number = {25},
  pages = {5664--5667},
  issn = {0031-9007, 1079-7114},
  doi = {10.1103/PhysRevLett.81.5664},
  urldate = {2024-11-24},
  copyright = {http://link.aps.org/licenses/aps-default-license},
  langid = {english}
}

@phdthesis{sohailUltrafastDynamicStudies2021,
  type = {Bachelor's {{Thesis}}},
  title = {Ultrafast Dynamic Studies in Spin-Filter Material {{Europium Oxide}} with {{Resonant Inelastic X-ray Scattering}}},
  author = {Sohail, Muhammad Zain},
  year = {2021},
  school = {Carl von Ossietzky Universit{\"a}t Oldenburg},
  file = {/Users/zain/Documents/Masters/Thesis/papers/Sohail - 2021 - Ultrafast dynamic studies in spin-filter material Europium Oxide with Resonant Inelastic X-ray Scatt.pdf}
}

@book{stohrMagnetismFundamentalsNanoscale2006,
  title = {Magnetism: {{From Fundamentals}} to {{Nanoscale Dynamics}}},
  author = {St{\"o}hr, J. and Siegmann, H. C.},
  year = {2006},
  publisher = {Springer Berlin Heidelberg},
  address = {Berlin, Heidelberg},
  isbn = {978-3-540-30283-4}
}

@inproceedings{sutskeverImportanceInitializationMomentum2013,
  title = {On the Importance of Initialization and Momentum in Deep Learning},
  booktitle = {Proceedings of the 30th {{International Conference}} on {{Machine Learning}}},
  author = {Sutskever, Ilya and Martens, James and Dahl, George and Hinton, Geoffrey},
  year = {2013},
  month = may,
  urldate = {2024-11-23},
  abstract = {Deep and recurrent neural networks (DNNs and RNNs respectively) are powerful models that were considered to be almost impossible to train using stochastic gradient descent with momentum. In this paper, we show that when stochastic gradient descent with momentum uses a well-designed random initialization and a particular type of slowly increasing schedule for the momentum parameter, it can train both DNNs and RNNs (on datasets with long-term dependencies) to levels of performance that were previously achievable only with Hessian-Free optimization. We find that both the initialization and the momentum are crucial since poorly initialized networks cannot be trained with momentum and well-initialized networks perform markedly worse when the momentum is absent or poorly tuned.     Our success training these models suggests that previous attempts to train deep and recurrent neural networks from random initializations have likely failed due to poor initialization schemes. Furthermore, carefully tuned momentum methods suffice for dealing with the curvature issues in deep and recurrent network training objectives without the need for sophisticated second-order methods.},
  langid = {english},
  file = {/Users/zain/Zotero/storage/ZSYLWLFD/Sutskever et al. - 2013 - On the importance of initialization and momentum in deep learning.pdf}
}

@article{svandrlikDevelopmentPerspectivesFERMI2017,
  title = {Development {{Perspectives}} at {{FERMI}}},
  author = {Svandrlik, M and Allaria, E and Badano, L and Bencivenga, F and Callegari, C and Capotondi, F and Cilento, F and Cinquegrana, P and Coreno, M and Cudin, I and D'Auria, G and Danailov, M B and Monte, R De and Ninno, G De and Delgiusto, P and Demidovich, A A and Fraia, M Di and Mitri, S Di and Diviacco, B and Fabris, A and Fabris, R and Fawley, W M and Ferianis, M and Radivo, P Furlan and Gaio, G and Gauthier, D and Gelmetti, F and Giannessi, L and Iazzourene, F and Krecic, S and Lonza, M and Mahne, N and Malvestuto, M and Masciovecchio, C and Milloch, M and Parmigiani, F and Penco, G and Perucchi, A and Pivetta, L and Plekan, O and Predonzani, M and Principi, E and Raimondi, L and Ribic, P Rebernik and Ribi{\v c}, P Rebernik and Rossi, F and Roussel, E and Rumiz, L and Scafuri, C and Serpico, C and Sigalotti, P and Trovo, M and Trov{\`o}, M and Vascotto, A and Veronese, M and Visintini, R and Zangrando, D and Zangrando, M},
  year = {2017},
  journal = {Free Electron Lasers},
  abstract = {FERMI is the seeded Free Electron Laser (FEL) user facility at the Elettra laboratory in Trieste, operating in the ultraviolet to soft X-rays spectral range; the radiation produced by the seeded FEL is characterized by a number of desirable properties, such as wavelength stability, low temporal jitter and longitudinal coherence. In this paper, after an overview of the FEL performances, we will present the development plans under consideration for the next 3 to 5 years. These include an upgrade of the linac and of the existing FEL lines, the possibility to perform multipulse experiments in different configurations and an Echo Enabled Harmonic Generation (EEHG) experiment on FEL-2, the FEL line extending to 4 nm (310 eV).},
  langid = {english},
  file = {/Users/zain/Zotero/storage/QL49YXDL/Svandrlik et al. - 2017 - Development Perspectives at FERMI.pdf}
}

@misc{TheoryPhotoelectronCounting,
  title = {Theory of Photoelectron Counting Statistics: {{An}} Essay - {{ScienceDirect}}},
  urldate = {2024-09-24},
  howpublished = {https://www.sciencedirect.com/science/article/pii/0370157380901210},
  file = {/Users/zain/Zotero/storage/YW86NCA2/Theory of photoelectron counting statistics An essay - ScienceDirect.pdf;/Users/zain/Zotero/storage/XVQA5SIL/0370157380901210.html}
}

@misc{thepandasdevelopmentteamPandasdevPandasPandas2024,
  title = {Pandas-Dev/Pandas: {{Pandas}}},
  shorttitle = {Pandas-Dev/Pandas},
  author = {{The pandas development team}},
  year = {2024},
  month = apr,
  doi = {10.5281/ZENODO.3509134},
  urldate = {2024-09-02},
  abstract = {Pandas is a powerful data structures for data analysis, time series, and statistics.},
  copyright = {BSD 3-Clause "New" or "Revised" License},
  howpublished = {Zenodo},
  keywords = {data science,python}
}

@book{tibshiraniElementsStatisticalLearning,
  title = {The {{Elements}} of {{Statistical Learning}}},
  author = {Tibshirani, Robert and Friedman, Jerome and Hastie, Trevor},
  langid = {english},
  file = {/Users/zain/Zotero/storage/2RXYCXXS/Tibshirani and Friedman - Valerie and Patrick Hastie.pdf}
}

@article{tiedtkeSoftXrayFreeelectron2009,
  title = {The Soft X-Ray Free-Electron Laser {{FLASH}} at {{DESY}}: Beamlines, Diagnostics and End-Stations},
  author = {Tiedtke, K. and Azima, A. and von Bargen, N. and others},
  year = {2009},
  month = feb,
  journal = {New Journal of Physics},
  volume = {11},
  number = {2},
  pages = {023029},
  publisher = {IOP Publishing},
  doi = {10.1088/1367-2630/11/2/023029},
  abstract = {FLASH, the Free-electron LASer in Hamburg, is a worldwide unique source for extremely bright ultra-short laser-like pulses tunable in a wide spectral range in the extreme ultraviolet and soft x-ray region (Ackermann et al 2007 Nat. Photonics 1 336--42). To fully exploit the features of this new generation of light sources, a user facility with efficient radiation transport to the experimental area and novel online photon diagnostics capable of characterizing the unique parameters of the FLASH radiation has been built. It serves a broad user community active in many scientific fields ranging from atomic and molecular physics to plasma and solid state physics as well as chemistry and biology. A special focus is placed on the exploitation of the ultra-short FLASH pulses using pump--probe techniques. Thus, the facility is equipped with optical and THz sources synchronized to FLASH. This paper gives a detailed overview of the FLASH user facility.}
}

@article{ulyanovDeepImagePrior2020,
  title = {Deep {{Image Prior}}},
  author = {Ulyanov, Dmitry and Vedaldi, Andrea and Lempitsky, Victor},
  year = {2020},
  month = mar,
  journal = {International Journal of Computer Vision},
  volume = {128},
  number = {7},
  pages = {1867--1888},
  publisher = {Springer},
  issn = {1573-1405},
  doi = {10.1007/s11263-020-01303-4},
  urldate = {2024-11-25},
  abstract = {Deep convolutional networks have become a popular tool for image generation and restoration. Generally, their excellent performance is imputed to their ability to learn realistic image priors from a large number of example images. In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning. In order to do so, we show that a randomly-initialized neural network can be used as a handcrafted prior with excellent results in standard inverse problems such as denoising, super-resolution, and inpainting. Furthermore, the same prior can be used to invert deep neural representations to diagnose them, and to restore images based on flash-no flash input pairs. Apart from its diverse applications, our approach highlights the inductive bias captured by standard generator network architectures. It also bridges the gap between two very popular families of image restoration methods: learning-based methods using deep convolutional networks and learning-free methods based on handcrafted image priors such as self-similarity (Code and supplementary material are available at https://dmitryulyanov.github.io/deep\_image\_prior).},
  copyright = {2020 Springer Science+Business Media, LLC, part of Springer Nature},
  langid = {english},
  file = {/Users/zain/Zotero/storage/58UXCZFX/s11263-020-01303-4.pdf}
}

@article{van_der_Walt_scikit-image_image_processing_2014,
  title = {Scikit-Image: Image Processing in {{Python}}},
  author = {{van der Walt}, St{\'e}fan J. and Sch{\"o}nberger, Johannes L. and {Nunez-Iglesias}, Juan and Boulogne, Fran{\c c}ois and Warner, Joshua D. and Yager, Neil and Gouillart, Emmanuelle and Yu, Tony and scikit-image {contributors}, the},
  year = {2014},
  month = jun,
  journal = {PeerJ},
  volume = {2},
  pages = {e453},
  doi = {10.7717/peerj.453}
}

@article{vanderwaltScikitimageImageProcessing2014,
  title = {Scikit-Image: Image Processing in {{Python}}},
  shorttitle = {Scikit-Image},
  author = {Van Der Walt, St{\'e}fan and Sch{\"o}nberger, Johannes L. and {Nunez-Iglesias}, Juan and Boulogne, Fran{\c c}ois and Warner, Joshua D. and Yager, Neil and Gouillart, Emmanuelle and Yu, Tony},
  year = {2014},
  month = jun,
  journal = {PeerJ},
  volume = {2},
  pages = {e453},
  issn = {2167-8359},
  doi = {10.7717/peerj.453},
  urldate = {2024-11-03},
  langid = {english},
  file = {/Users/zain/Zotero/storage/9NLSM749/Van Der Walt et al. - 2014 - scikit-image image processing in Python.pdf}
}

@article{wangFPGABasedHardwareAccelerator2020,
  title = {An {{FPGA-Based Hardware Accelerator}} for {{Real-Time Block-Matching}} and {{3D Filtering}}},
  author = {Wang, Dong and Xu, Jia and Xu, Ke},
  year = {2020},
  journal = {IEEE Access},
  volume = {8},
  pages = {121987--121998},
  issn = {2169-3536},
  doi = {10.1109/ACCESS.2020.3006773},
  urldate = {2024-10-18},
  abstract = {Block-matching and 3D filtering (BM3D) denoising algorithm has been employed in many application fields because of its superior image processing quality. Due to the huge computational workload, real-time implementation of this algorithm is very challenging. Recently, studies on accelerating the BM3D algorithm on GPU have presented impressive speed up over CPU-based implementations. However, GPU devices are generally inefficient in energy dissipation and, thus, are not suitable for embedded application scenarios. In this paper, we propose a dedicated hardware accelerator design to efficiently boost the BM3D algorithm with reduced power consumption on FPGA device. The proposed design is based on a deeply pipelined OpenCL kernel architecture that can efficiently speed up the compute-intensive procedures of the denoising algorithm by exploiting the intrinsic parallelism and maximizing data reuse. The final design was implemented on Intel's Arria-10 GX1150 FPGA, and achieved an average 1.2{\texttimes} performance boost and an outstanding 8.3{\texttimes} reduction in energy dissipation when compared to a state-of-the-art GPU-based software design.},
  keywords = {Acceleration,BM3D,Field programmable gate arrays,FPGA,Graphics processing units,Hardware,image denoising,Kernel,Real-time systems,systolic array,Three-dimensional displays},
  file = {/Users/zain/Zotero/storage/WD9HLHMX/Wang et al. - 2020 - An FPGA-Based Hardware Accelerator for Real-Time Block-Matching and 3D Filtering.pdf;/Users/zain/Zotero/storage/7FUCYJN8/9133090.html}
}

@inproceedings{wangMultiscaleStructuralSimilarity2003,
  title = {Multiscale Structural Similarity for Image Quality Assessment},
  booktitle = {The {{Thrity-Seventh Asilomar Conference}} on {{Signals}}, {{Systems}} \& {{Computers}}, 2003},
  author = {Wang, Z. and Simoncelli, E.P. and Bovik, A.C.},
  year = {2003},
  month = nov,
  volume = {2},
  pages = {1398-1402 Vol.2},
  doi = {10.1109/ACSSC.2003.1292216},
  urldate = {2024-09-30},
  abstract = {The structural similarity image quality paradigm is based on the assumption that the human visual system is highly adapted for extracting structural information from the scene, and therefore a measure of structural similarity can provide a good approximation to perceived image quality. This paper proposes a multiscale structural similarity method, which supplies more flexibility than previous single-scale methods in incorporating the variations of viewing conditions. We develop an image synthesis method to calibrate the parameters that define the relative importance of different scales. Experimental comparisons demonstrate the effectiveness of the proposed method.},
  keywords = {Data mining,Displays,Distortion measurement,Electric variables measurement,Humans,Image quality,Layout,Optical filters,Signal processing,Visual system},
  file = {/Users/zain/Zotero/storage/7RJLWK2D/Wang et al. - 2003 - Multiscale structural similarity for image quality assessment.pdf;/Users/zain/Zotero/storage/IE9MAFTC/1292216.html}
}

@article{waskomSeabornStatisticalData2021,
  title = {Seaborn: Statistical Data Visualization},
  shorttitle = {Seaborn},
  author = {Waskom, Michael},
  year = {2021},
  month = apr,
  journal = {Journal of Open Source Software},
  volume = {6},
  number = {60},
  pages = {3021},
  issn = {2475-9066},
  doi = {10.21105/joss.03021},
  urldate = {2024-09-02},
  copyright = {http://creativecommons.org/licenses/by/4.0/},
  file = {/Users/zain/Zotero/storage/ECPCTZ45/Waskom - 2021 - seaborn statistical data visualization.pdf}
}

@article{wetzsteinEE367CS,
  title = {{{EE}} 367 / {{CS 448I Computational Imaging}} and {{Display Notes}}: {{Noise}}, {{Denoising}}, and {{Image Reconstruction}} with {{Noise}} (Lecture 10)},
  author = {Wetzstein, Gordon},
  langid = {english},
  file = {/Users/zain/Zotero/storage/UW5VH5TN/Wetzstein - EE 367  CS 448I Computational Imaging and Display Notes Noise, Denoising, and Image Reconstruction.pdf}
}

@article{wolnyAccurateVersatile3D2020,
  title = {Accurate and Versatile {{3D}} Segmentation of Plant Tissues at Cellular Resolution},
  author = {Wolny, Adrian and Cerrone, Lorenzo and Vijayan, Athul and Tofanelli, Rachele and Barro, Amaya Vilches and Louveaux, Marion and Wenzl, Christian and Strauss, S{\"o}ren and {Wilson-S{\'a}nchez}, David and Lymbouridou, Rena and Steigleder, Susanne S and Pape, Constantin and Bailoni, Alberto and {Duran-Nebreda}, Salva and Bassel, George W and Lohmann, Jan U and Tsiantis, Miltos and Hamprecht, Fred A and Schneitz, Kay and Maizel, Alexis and Kreshuk, Anna},
  year = {2020},
  month = jul,
  journal = {eLife},
  volume = {9},
  pages = {e57613},
  issn = {2050-084X},
  doi = {10.7554/eLife.57613},
  urldate = {2024-10-04},
  abstract = {Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge. Here, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells. PlantSeg employs a convolutional neural network to predict cell boundaries and graph partitioning to segment cells based on the neural network predictions. PlantSeg was trained on fixed and live plant organs imaged with confocal and light sheet microscopes. PlantSeg delivers accurate results and generalizes well across different tissues, scales, acquisition settings even on non plant samples. We present results of PlantSeg applications in diverse developmental contexts. PlantSeg is free and open-source, with both a command line and a user-friendly graphical interface.},
  langid = {english},
  file = {/Users/zain/Zotero/storage/HH9BYDI8/Wolny et al. - 2020 - Accurate and versatile 3D segmentation of plant tissues at cellular resolution.pdf}
}

@misc{wuGroupNormalization2018,
  title = {Group {{Normalization}}},
  author = {Wu, Yuxin and He, Kaiming},
  year = {2018},
  publisher = {arXiv},
  doi = {10.48550/ARXIV.1803.08494},
  urldate = {2024-11-24},
  abstract = {Batch Normalization (BN) is a milestone technique in the development of deep learning, enabling various networks to train. However, normalizing along the batch dimension introduces problems --- BN's error increases rapidly when the batch size becomes smaller, caused by inaccurate batch statistics estimation. This limits BN's usage for training larger models and transferring features to computer vision tasks including detection, segmentation, and video, which require small batches constrained by memory consumption. In this paper, we present Group Normalization (GN) as a simple alternative to BN. GN divides the channels into groups and computes within each group the mean and variance for normalization. GN's computation is independent of batch sizes, and its accuracy is stable in a wide range of batch sizes. On ResNet-50 trained in ImageNet, GN has 10.6\% lower error than its BN counterpart when using a batch size of 2; when using typical batch sizes, GN is comparably good with BN and outperforms other normalization variants. Moreover, GN can be naturally transferred from pre-training to fine-tuning. GN can outperform its BN-based counterparts for object detection and segmentation in COCO, and for video classification in Kinetics, showing that GN can effectively replace the powerful BN in a variety of tasks. GN can be easily implemented by a few lines of code in modern libraries.},
  copyright = {arXiv.org perpetual, non-exclusive license},
  keywords = {Computer Vision and Pattern Recognition (cs.CV),FOS: Computer and information sciences,Machine Learning (cs.LG)}
}

@article{xianOpensourceEndtoendWorkflow2020,
  title = {An Open-Source, End-to-End Workflow for Multidimensional Photoemission Spectroscopy},
  author = {Xian, R. Patrick and Acremann, Yves and Agustsson, Steinn Y. and Dendzik, Maciej and B{\"u}hlmann, Kevin and Curcio, Davide and Kutnyakhov, Dmytro and Pressacco, Federico and Heber, Michael and Dong, Shuo and Pincelli, Tommaso and Demsar, Jure and Wurth, Wilfried and Hofmann, Philip and Wolf, Martin and Scheidgen, Markus and Rettig, Laurenz and Ernstorfer, Ralph},
  year = {2020},
  month = dec,
  journal = {Scientific Data},
  volume = {7},
  number = {1},
  pages = {442},
  publisher = {Nature Publishing Group},
  issn = {2052-4463},
  doi = {10.1038/s41597-020-00769-8},
  urldate = {2024-08-25},
  abstract = {Characterization of the electronic band structure of solid state materials is routinely performed using photoemission spectroscopy. Recent advancements in short-wavelength light sources and electron detectors give rise to multidimensional photoemission spectroscopy, allowing parallel measurements of the electron spectral function simultaneously in energy, two momentum components and additional physical parameters with single-event detection capability. Efficient processing of the photoelectron event streams at a rate of up to tens of megabytes per second will enable rapid band mapping for materials characterization. We describe an open-source workflow that allows user interaction with billion-count single-electron events in photoemission band mapping experiments, compatible with beamlines at 3rd and 4rd generation light sources and table-top laser-based setups. The workflow offers an end-to-end recipe from distributed operations on single-event data to structured formats for downstream scientific tasks and storage to materials science database integration. Both the workflow and processed data can be archived for reuse, providing the infrastructure for documenting the provenance and lineage of photoemission data for future high-throughput experiments.},
  copyright = {2020 The Author(s)},
  langid = {english},
  keywords = {Characterization and analytical techniques,Electronic properties and materials},
  file = {/Users/zain/Zotero/storage/P6C7P5RB/Xian et al. - 2020 - An open-source, end-to-end workflow for multidimensional photoemission spectroscopy.pdf}
}

@article{xuDataScienceConnotation2021,
  title = {Data Science: Connotation, Methods, Technologies, and Development},
  shorttitle = {Data Science},
  author = {Xu, Zongben and Tang, Niansheng and Xu, Chen and Cheng, Xueqi},
  year = {2021},
  month = mar,
  journal = {Data Science and Management},
  volume = {1},
  number = {1},
  pages = {32--37},
  issn = {2666-7649},
  doi = {10.1016/j.dsm.2021.02.002},
  urldate = {2024-08-31},
  abstract = {The rapid development of big data breeds data science. Understanding and mastering the internal pattern of the value generation of big data is important for improving digitization and the covergence of data science with management science, computer science, and other disciplines. In this study, we discuss the significance of data science for the development of science and technology and social progress. Based on the interpretation of the connotation of data science, we introduce the definition of data science and review its formation by summarizing the major progress of related disciplines. We also discuss the research methodologies, development patterns and trends, and technical directions of data science. Finally, we make suggestions for data science to promote the development of data-based science and technology.},
  keywords = {big data,data science,data science methodology,technical directions}
}

@book{yaroslavskyDigitalPictureProcessing1985,
  title = {Digital {{Picture Processing}}. {{An Introduction}}},
  author = {Yaroslavsky, Leonid P.},
  year = {1985},
  series = {Springer {{Series}} in {{Information Sciences}}},
  edition = {With 87 figures. Title of the original russian edition: Vvedynie v Tsifrovuyu Obrabotku Izebrazheny, Moscow, 1979},
  number = {9},
  publisher = {Springer},
  address = {Berlin},
  isbn = {978-3-540-11934-0},
  file = {/Users/zain/Zotero/storage/SWLCPCG4/Yaroslavsky - 1985 - Digital Picture Processing. An Introduction.PDF}
}

@book{yuFundamentalsSemiconductorsPhysics2010,
  title = {Fundamentals of Semiconductors: Physics and Materials Properties},
  shorttitle = {Fundamentals of Semiconductors},
  author = {Yu, Peter Y. and Cardona, Manuel},
  year = {2010},
  series = {Graduate Texts in Physics},
  edition = {Fourth edition},
  publisher = {Springer},
  address = {Heidelberg Dordrecht London New York},
  isbn = {978-3-642-00709-5},
  langid = {english},
  keywords = {ARPES,Physics},
  file = {/Users/zain/Documents/Masters/Thesis/books/Yu and Cardona - 2010 - Fundamentals of semiconductors physics and materials properties.pdf}
}

@article{zhangGaussianDenoiserResidual2017,
  title = {Beyond a {{Gaussian Denoiser}}: {{Residual Learning}} of {{Deep CNN}} for {{Image Denoising}}},
  shorttitle = {Beyond a {{Gaussian Denoiser}}},
  author = {Zhang, Kai and Zuo, Wangmeng and Chen, Yunjin and Meng, Deyu and Zhang, Lei},
  year = {2017},
  month = jul,
  journal = {IEEE Transactions on Image Processing},
  volume = {26},
  number = {7},
  pages = {3142--3155},
  issn = {1941-0042},
  doi = {10.1109/TIP.2017.2662206},
  urldate = {2024-10-21},
  abstract = {The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image denoising. Specifically, residual learning and batch normalization are utilized to speed up the training process as well as boost the denoising performance. Different from the existing discriminative denoising models which usually train a specific model for additive white Gaussian noise at a certain noise level, our DnCNN model is able to handle Gaussian denoising with unknown noise level (i.e., blind Gaussian denoising). With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers. This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks, such as Gaussian denoising, single image super-resolution, and JPEG image deblocking. Our extensive experiments demonstrate that our DnCNN model can not only exhibit high effectiveness in several general image denoising tasks, but also be efficiently implemented by benefiting from GPU computing.},
  keywords = {batch normalization,Computational modeling,convolutional neural networks,Image denoising,Neural networks,Noise level,Noise reduction,residual learning,Training,Transform coding},
  file = {/Users/zain/Zotero/storage/TAM39XBT/Zhang et al. - 2017 - Beyond a Gaussian Denoiser Residual Learning of Deep CNN for Image Denoising.pdf;/Users/zain/Zotero/storage/GAZACLJX/7839189.html}
}