Add SR dependent bad PMT list (#184)

XENON1T · Dec 19, 2017 · eaea8a1 · eaea8a1
1 parent 5bf6e1f
commit eaea8a1
Showing 1 changed file with 18 additions and 6 deletions.
diff --git a/hax/treemakers/posrec.py b/hax/treemakers/posrec.py
@@ -128,11 +128,6 @@ def __init__(self):
         # Threshold for s1_aft probability calculation
         self.low_pe_threshold = 10
 
-        self.list_bad_pmts = [1, 2, 12, 26, 34, 62, 65, 79, 86, 88, 102, 118,
-                              130, 134, 135, 139, 148, 150, 152, 162, 178, 183,
-                              190, 198, 206, 213, 214, 234, 239, 244, 27, 73,
-                              91, 137, 167, 203]
-
         self.ntop_pmts = len(self.pax_config['DEFAULT']['channels_top'])
 
     def get_data(self, dataset, event_list=None):
@@ -187,11 +182,28 @@ def extract_data(self, event):
         s2 = event.peaks[interaction.s2]
 
         # Position reconstruction based on NN from TensorFlow
+
+        # Hardcode warning, this should come with NN model eventually
+        # xenon:xenon1t:analysis:sciencerun1:pmt#list_of_pmts_excluded_from_sr1_analysis_shingo
+        if self.run_number <= 6385:  # SR0
+            list_bad_pmts = [1, 12, 26, 34, 51, 62, 65, 79, 86, 88, 102, 118,
+                             130, 134, 135, 139, 148, 150, 152, 162, 178, 183,
+                             198, 206, 213, 214, 234, 239, 244, 27, 73,
+                             91, 137, 167, 203]
+
+        else:  # SR1
+            list_bad_pmts = [1, 2, 12, 26, 34, 62, 65, 79, 86, 88, 102, 118,
+                             130, 134, 135, 139, 148, 150, 152, 162, 178, 183,
+                             190, 198, 206, 213, 214, 234, 239, 244, 27, 73,
+                             91, 137, 167, 203]
+
         s2apc = np.array(list(s2.area_per_channel))
         s2apc_clean = []
+
         for ipmt, s2_t in enumerate(s2apc):
-            if ipmt not in self.list_bad_pmts and ipmt < self.ntop_pmts:
+            if ipmt not in list_bad_pmts and ipmt < self.ntop_pmts:
                 s2apc_clean.append(s2_t)
+
         s2apc_clean = np.asarray(s2apc_clean)
         s2apc_clean_norm = s2apc_clean / s2apc_clean.sum()
         s2apc_clean_norm = s2apc_clean_norm.reshape(1, len(s2apc_clean_norm))