diff --git a/pax/config/XENON1T.ini b/pax/config/XENON1T.ini index 3e2470e8..b56f19db 100644 --- a/pax/config/XENON1T.ini +++ b/pax/config/XENON1T.ini @@ -242,7 +242,7 @@ s1_detection_efficiency = 0.12 # Monte Carlo paper dra # This should include LCE and QE effects, but not zero-length encoding / hitfinder acceptance. s1_patterns_file = 'XENON1T_s1_xyz_patterns_Feb2017.json.gz' s2_patterns_file = 'XENON1T_s2_xy_patterns_top_updatedgeo.json.gz' -s2_mean_area_fraction_top = 0.56 # See XeAnalysisScripts/mapmaking +s2_mean_area_fraction_top = 0.678 # See Issue: https://github.com/XENON1T/pax/issues/491 # Various data-extracted corrections rz_position_distortion_map = 'transverse_field_rz_correction_12kv.json.gz' @@ -261,6 +261,14715 @@ s2_patterns_zoom_factor = 2 # The updated AP info for each PMT is from Julien's analysis # The root file can be found on Midway under: # /project/lgrandi/julienw/AP_Run_5441.root +<<<<<<< HEAD +# Also the uniformly time-distributed after pulse is included +each_pmt_afterpulse_types = { + 0: { + 'N2': {'p': 6.775832837384661e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.409572520005186* us, + scale = 0.05*us)}, + 'Uniform2': {'p': 0.0008214211387075089, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=400.0* ns, + high = 500.0*ns)}, + 'He': {'p': 0.0002668727143847117, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=0.5554465538598814* us, + scale = 0.04*us)}, + 'Ne': {'p': 0.00011114743338516945, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.1920135282892552* us, + scale = 0.045*us)}, + 'Uniform8': {'p': 0.007670361646179743, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=1000.0* ns, + high = 5000.0*ns)}, + 'Uniform5': {'p': 0.00045826027347575213, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=700.0* ns, + high = 800.0*ns)}, + 'Uniform3': {'p': 0.0006645071151049168, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=500.0* ns, + high = 600.0*ns)}, + 'Uniform0': {'p': 0.0014342179505797534, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=200.0* ns, + high = 300.0*ns)}, + 'Xe2+': {'p': 7.548515529367123e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.1590764269702345* us, + scale = 0.065*us)}, + 'Uniform6': {'p': 0.0003994175146247801, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=800.0* ns, + high = 900.0*ns)}, + 'CH4': {'p': 6.894707097689655e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.0608931077197628* us, + scale = 0.04*us)}, + 'Uniform1': {'p': 0.001060952773222072, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=300.0* ns, + high = 400.0*ns)}, + 'Uniform7': {'p': 0.00035127343920125743, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=900.0* ns, + high = 1000.0*ns)}, + 'Uniform4': {'p': 0.0005616808799410969, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=600.0* ns, + high = 700.0*ns)}, + 'Ar': {'p': 0.0004035781137354549, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.6964778657914972* us, + scale = 0.055*us)}, + 'Xe': {'p': 0.0003833694894836058, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=3.0686902411690897* us, + scale = 0.09*us)}, + }, + 1: { + }, + 2: { + 'N2': {'p': 0.00017226001634032428, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.3936775723872181* us, + scale = 0.05*us)}, + 'Uniform2': {'p': 0.001100712307801733, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=400.0* ns, + high = 500.0*ns)}, + 'He': {'p': 0.00029488579068428394, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=0.5494388245658194* us, + scale = 0.04*us)}, + 'Ne': {'p': 0.00022831751318327728, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.178579866660297* us, + scale = 0.045*us)}, + 'Uniform8': {'p': 0.04765879916490931, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=1000.0* ns, + high = 5000.0*ns)}, + 'Uniform5': {'p': 0.0006183842620488252, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=700.0* ns, + high = 800.0*ns)}, + 'Uniform3': {'p': 0.0009529774463302009, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=500.0* ns, + high = 600.0*ns)}, + 'Uniform0': {'p': 0.0022347087543539697, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=200.0* ns, + high = 300.0*ns)}, + 'Xe2+': {'p': 0.0050078030513038, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.134858498263189* us, + scale = 0.065*us)}, + 'Uniform6': {'p': 0.0005378016103370802, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=800.0* ns, + high = 900.0*ns)}, + 'CH4': {'p': 0.00012846509693176727, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.0488776491316387* us, + scale = 0.04*us)}, + 'Uniform1': {'p': 0.001500121972807773, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=300.0* ns, + high = 400.0*ns)}, + 'Uniform7': {'p': 0.00046189041669558137, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=900.0* ns, + high = 1000.0*ns)}, + 'Uniform4': {'p': 0.0007293313912171696, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=600.0* ns, + high = 700.0*ns)}, + 'Ar': {'p': 0.0026860883903914974, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.6774797399495838* us, + scale = 0.055*us)}, + 'Xe': {'p': 0.030334112979142933, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=3.034309416684871* us, + scale = 0.09*us)}, + }, + 3: { + 'N2': {'p': 3.439445134866278e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.2805169795219462* us, + scale = 0.05*us)}, + 'Uniform2': {'p': 0.0012764884113739565, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=400.0* ns, + high = 500.0*ns)}, + 'He': {'p': 8.825746006449318e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=0.5066681137066271* us, + scale = 0.04*us)}, + 'Ne': {'p': 3.893711473433522e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.0829418594883853* us, + scale = 0.045*us)}, + 'Uniform8': {'p': 0.006428517642638743, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=1000.0* ns, + high = 5000.0*ns)}, + 'Uniform5': {'p': 0.0006723141810795215, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=700.0* ns, + high = 800.0*ns)}, + 'Uniform3': {'p': 0.001025992973249733, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=500.0* ns, + high = 600.0*ns)}, + 'Uniform0': {'p': 0.0022713316928362213, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=200.0* ns, + high = 300.0*ns)}, + 'Xe2+': {'p': 2.076646119164545e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.9624442672555813* us, + scale = 0.065*us)}, + 'Uniform6': {'p': 0.0005853546248395062, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=800.0* ns, + high = 900.0*ns)}, + 'CH4': {'p': 3.50434032609017e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=0.9633362274132542* us, + scale = 0.04*us)}, + 'Uniform1': {'p': 0.001582144762038488, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=300.0* ns, + high = 400.0*ns)}, + 'Uniform7': {'p': 0.0005120230587565081, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=900.0* ns, + high = 1000.0*ns)}, + 'Uniform4': {'p': 0.0008021045635273055, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=600.0* ns, + high = 700.0*ns)}, + 'Ar': {'p': 4.47776819444855e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.5422267504292084* us, + scale = 0.055*us)}, + 'Xe': {'p': 1.752170163045085e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.7895426785149993* us, + scale = 0.09*us)}, + }, + 4: { + 'N2': {'p': 7.039660844115181e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.3324364195015244* us, + scale = 0.05*us)}, + 'Uniform2': {'p': 0.0009223055652797782, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=400.0* ns, + high = 500.0*ns)}, + 'He': {'p': 0.0002353886594751014, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=0.5262918298706241* us, + scale = 0.04*us)}, + 'Ne': {'p': 9.789528361347675e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.1268217265640232* us, + scale = 0.045*us)}, + 'Uniform8': {'p': 0.006198201383842038, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=1000.0* ns, + high = 5000.0*ns)}, + 'Uniform5': {'p': 0.0005197249607569412, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=700.0* ns, + high = 800.0*ns)}, + 'Uniform3': {'p': 0.000784262215914707, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=500.0* ns, + high = 600.0*ns)}, + 'Uniform0': {'p': 0.0016444207753050308, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=200.0* ns, + high = 300.0*ns)}, + 'Xe2+': {'p': 4.124801275848739e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.0415499885297916* us, + scale = 0.065*us)}, + 'Uniform6': {'p': 0.0004707773189502028, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=800.0* ns, + high = 900.0*ns)}, + 'CH4': {'p': 5.2247482827417367e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.002583659741248* us, + scale = 0.04*us)}, + 'Uniform1': {'p': 0.001152744463223861, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=300.0* ns, + high = 400.0*ns)}, + 'Uniform7': {'p': 0.000390481187447014, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=900.0* ns, + high = 1000.0*ns)}, + 'Uniform4': {'p': 0.0006495187075703149, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=600.0* ns, + high = 700.0*ns)}, + 'Ar': {'p': 0.0005433738214051406, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.6042824475023254* us, + scale = 0.055*us)}, + 'Xe': {'p': 2.254891364130644e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.901844599613433* us, + scale = 0.09*us)}, + }, + 5: { + 'N2': {'p': 7.148373981969381e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.2728650791139462* us, + scale = 0.05*us)}, + 'Uniform2': {'p': 0.0024691953492858723, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=400.0* ns, + high = 500.0*ns)}, + 'He': {'p': 0.0001255975989355368, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=0.503775965374888* us, + scale = 0.04*us)}, + 'Ne': {'p': 7.281988448922079e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.0764748334012177* us, + scale = 0.045*us)}, + 'Uniform8': {'p': 0.014621431118633819, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=1000.0* ns, + high = 5000.0*ns)}, + 'Uniform5': {'p': 0.0014730994981535033, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=700.0* ns, + high = 800.0*ns)}, + 'Uniform3': {'p': 0.0020676838760930127, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=500.0* ns, + high = 600.0*ns)}, + 'Uniform0': {'p': 0.004221549083370516, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=200.0* ns, + high = 300.0*ns)}, + 'Xe2+': {'p': 7.682831849780176e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.950785645723235* us, + scale = 0.065*us)}, + 'Uniform6': {'p': 0.0012786904487373267, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=800.0* ns, + high = 900.0*ns)}, + 'CH4': {'p': 3.540783374246516e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=0.957551930749776* us, + scale = 0.04*us)}, + 'Uniform1': {'p': 0.00308649418660734, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=300.0* ns, + high = 400.0*ns)}, + 'Uniform7': {'p': 0.0010916301950035484, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=900.0* ns, + high = 1000.0*ns)}, + 'Uniform4': {'p': 0.0017610386744365692, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=600.0* ns, + high = 700.0*ns)}, + 'Ar': {'p': 5.5450003785369966e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.5330809658456444* us, + scale = 0.055*us)}, + 'Xe': {'p': 0.00038480966482377227, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.7729915925208073* us, + scale = 0.09*us)}, + }, + 6: { + 'N2': {'p': 4.896844725907121e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.3489687879824355* us, + scale = 0.05*us)}, + 'Uniform2': {'p': 0.0011106043838357352, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=400.0* ns, + high = 500.0*ns)}, + 'He': {'p': 0.00018216262380374494, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=0.532540481757386* us, + scale = 0.04*us)}, + 'Ne': {'p': 5.8109224080764505e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.1407941063060465* us, + scale = 0.045*us)}, + 'Uniform8': {'p': 0.006479504941320303, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=1000.0* ns, + high = 5000.0*ns)}, + 'Uniform5': {'p': 0.0006039441828618782, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=700.0* ns, + high = 800.0*ns)}, + 'Uniform3': {'p': 0.0009225655463609016, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=500.0* ns, + high = 600.0*ns)}, + 'Uniform0': {'p': 0.0020122767260327664, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=200.0* ns, + high = 300.0*ns)}, + 'Xe2+': {'p': 2.219902942411228e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.066739107163735* us, + scale = 0.065*us)}, + 'Uniform6': {'p': 0.0005399587451100252, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=800.0* ns, + high = 900.0*ns)}, + 'CH4': {'p': 3.525728202653127e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.015080963514772* us, + scale = 0.04*us)}, + 'Uniform1': {'p': 0.0014435898251974194, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=300.0* ns, + high = 400.0*ns)}, + 'Uniform7': {'p': 0.0004616092294955113, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=900.0* ns, + high = 1000.0*ns)}, + 'Uniform4': {'p': 0.0007874126319258651, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=600.0* ns, + high = 700.0*ns)}, + 'Ar': {'p': 0.00019391505114592201, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.6240424381870497* us, + scale = 0.055*us)}, + 'Xe': {'p': 1.4364077862660889e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.9376041675410494* us, + scale = 0.09*us)}, + }, + 7: { + 'N2': {'p': 3.247479423882697e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.3317161564641247* us, + scale = 0.05*us)}, + 'Uniform2': {'p': 0.0008642723648560541, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=400.0* ns, + high = 500.0*ns)}, + 'He': {'p': 0.00017639717779726466, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=0.5260195958593384* us, + scale = 0.04*us)}, + 'Ne': {'p': 5.535476290709142e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.1262129941440853* us, + scale = 0.045*us)}, + 'Uniform8': {'p': 0.004949453867399401, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=1000.0* ns, + high = 5000.0*ns)}, + 'Uniform5': {'p': 0.0004753128974955584, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=700.0* ns, + high = 800.0*ns)}, + 'Uniform3': {'p': 0.0007114932192324818, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=500.0* ns, + high = 600.0*ns)}, + 'Uniform0': {'p': 0.0015742894570776804, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=200.0* ns, + high = 300.0*ns)}, + 'Xe2+': {'p': 1.8451587635697142e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.0404525782514247* us, + scale = 0.065*us)}, + 'Uniform6': {'p': 0.00044062391274044774, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=800.0* ns, + high = 900.0*ns)}, + 'CH4': {'p': 3.247479423882697e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.0020391917186768* us, + scale = 0.04*us)}, + 'Uniform1': {'p': 0.00114842681444579, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=300.0* ns, + high = 400.0*ns)}, + 'Uniform7': {'p': 0.0003815788323062169, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=900.0* ns, + high = 1000.0*ns)}, + 'Uniform4': {'p': 0.0005882366138260249, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=600.0* ns, + high = 700.0*ns)}, + 'Ar': {'p': 0.00015204108211814445, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.603421567167726* us, + scale = 0.055*us)}, + 'Xe': {'p': 1.5499333613985597e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.90028666827157* us, + scale = 0.09*us)}, + }, + 8: { + 'N2': {'p': 6.1533138471854e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.3259940072815881* us, + scale = 0.05*us)}, + 'Uniform2': {'p': 0.001283405459555812, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=400.0* ns, + high = 500.0*ns)}, + 'He': {'p': 0.0001604256824444765, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=0.5238568253932026* us, + scale = 0.04*us)}, + 'Ne': {'p': 6.81259747366955e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.1213769029686906* us, + scale = 0.045*us)}, + 'Uniform8': {'p': 0.007934112176055367, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=1000.0* ns, + high = 5000.0*ns)}, + 'Uniform5': {'p': 0.000760373782545053, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=700.0* ns, + high = 800.0*ns)}, + 'Uniform3': {'p': 0.0010467958913842781, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=500.0* ns, + high = 600.0*ns)}, + 'Uniform0': {'p': 0.002229843732241947, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=200.0* ns, + high = 300.0*ns)}, + 'Xe2+': {'p': 3.7359405500768495e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.031734172611818* us, + scale = 0.065*us)}, + 'Uniform6': {'p': 0.0006197266088951009, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=800.0* ns, + high = 900.0*ns)}, + 'CH4': {'p': 4.761492857941083e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=0.9977136507864051* us, + scale = 0.04*us)}, + 'Uniform1': {'p': 0.001596198913454403, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=300.0* ns, + high = 400.0*ns)}, + 'Uniform7': {'p': 0.0005457403352563241, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=900.0* ns, + high = 1000.0*ns)}, + 'Uniform4': {'p': 0.0008534060276155942, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=600.0* ns, + high = 700.0*ns)}, + 'Ar': {'p': 0.00022195882091633048, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.596582280064122* us, + scale = 0.055*us)}, + 'Xe': {'p': 2.124358352004483e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.8879096405866904* us, + scale = 0.09*us)}, + }, + 9: { + 'N2': {'p': 4.075509863940459e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.331764139230829* us, + scale = 0.05*us)}, + 'Uniform2': {'p': 0.0014056934021942002, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=400.0* ns, + high = 500.0*ns)}, + 'He': {'p': 0.0001694554101322612, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=0.5260377316519228* us, + scale = 0.04*us)}, + 'Ne': {'p': 4.862011767507915e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.1262535469201889* us, + scale = 0.045*us)}, + 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'normal', + 'time_parameters': dict(loc=2.8983167547954505* us, + scale = 0.09*us)}, + }, + 19: { + 'N2': {'p': 2.0054038246381368e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.3321962075826972* us, + scale = 0.05*us)}, + 'Uniform2': {'p': 0.0008674988802708844, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=400.0* ns, + high = 500.0*ns)}, + 'He': {'p': 0.0002050687136807385, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=0.5262010382419754* us, + scale = 0.04*us)}, + 'Ne': {'p': 3.493284081627722e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.1266187107558352* us, + scale = 0.045*us)}, + 'Uniform8': {'p': 0.004964344822668731, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=1000.0* ns, + high = 5000.0*ns)}, + 'Uniform5': {'p': 0.0005149359498103087, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=700.0* ns, + high = 800.0*ns)}, + 'Uniform3': {'p': 0.000731002039303579, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=500.0* ns, + high = 600.0*ns)}, + 'Uniform0': {'p': 0.0015583928430817006, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=200.0* ns, + high = 300.0*ns)}, + 'Xe2+': {'p': 1.1644280272092407e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.0411839958090505* us, + scale = 0.065*us)}, + 'Uniform6': {'p': 0.00043536670128434393, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=800.0* ns, + high = 900.0*ns)}, + 'CH4': {'p': 2.1994751625063438e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.0024020764839507* us, + scale = 0.04*us)}, + 'Uniform1': {'p': 0.0011262606640951602, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=300.0* ns, + high = 400.0*ns)}, + 'Uniform7': {'p': 0.0003907302935746564, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=900.0* ns, + high = 1000.0*ns)}, + 'Uniform4': {'p': 0.0005873892492811059, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=600.0* ns, + high = 700.0*ns)}, + 'Ar': {'p': 1.81133248676993e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.6039953388957235* us, + scale = 0.055*us)}, + 'Xe': {'p': 1.2291184731653097e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.9013250204352747* us, + scale = 0.09*us)}, + }, + 20: { + 'N2': {'p': 3.144386552825677e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.332004127531068* us, + scale = 0.05*us)}, + 'Uniform2': {'p': 0.0012762510126174806, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=400.0* ns, + high = 500.0*ns)}, + 'He': {'p': 0.00021085886295419244, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=0.5261284387606362* us, + scale = 0.04*us)}, + 'Ne': {'p': 3.8225875740233715e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.1264563737364706* us, + scale = 0.045*us)}, + 'Uniform8': {'p': 0.0066697987702878745, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=1000.0* ns, + high = 5000.0*ns)}, + 'Uniform5': {'p': 0.0007491038552319996, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=700.0* ns, + high = 800.0*ns)}, + 'Uniform3': {'p': 0.0010290159130717754, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=500.0* ns, + high = 600.0*ns)}, + 'Uniform0': {'p': 0.002277522338458441, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=200.0* ns, + high = 300.0*ns)}, + 'Xe2+': {'p': 1.9112937870116857e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.0408913379715914* us, + scale = 0.065*us)}, + 'Uniform6': {'p': 0.0006455240629036242, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=800.0* ns, + high = 900.0*ns)}, + 'CH4': {'p': 1.7263298721395873e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.0022568775212723* us, + scale = 0.04*us)}, + 'Uniform1': {'p': 0.0015721932764128383, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=300.0* ns, + high = 400.0*ns)}, + 'Uniform7': {'p': 0.0005215982399393182, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=900.0* ns, + high = 1000.0*ns)}, + 'Uniform4': {'p': 0.0008304879777757229, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=600.0* ns, + high = 700.0*ns)}, + 'Ar': {'p': 2.589494808209381e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.6037657589637677* us, + scale = 0.055*us)}, + 'Xe': {'p': 1.2947474041046905e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.90090955064571* us, + scale = 0.09*us)}, + }, + 21: { + 'N2': {'p': 0.0014518099904292478, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.3307575421084323* us, + scale = 0.05*us)}, + 'Uniform2': {'p': 0.0015434228907893757, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=400.0* ns, + high = 500.0*ns)}, + 'He': {'p': 0.00028629031362539944, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=0.5256572734607485* us, + scale = 0.04*us)}, + 'Ne': {'p': 0.0004408870829831152, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.1254028184079616* us, + scale = 0.045*us)}, + 'Uniform8': {'p': 0.023019013616876007, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=1000.0* ns, + high = 5000.0*ns)}, + 'Uniform5': {'p': 0.0008595071415731436, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=700.0* ns, + high = 800.0*ns)}, + 'Uniform3': {'p': 0.0012520429715884135, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=500.0* ns, + high = 600.0*ns)}, + 'Uniform0': {'p': 0.0028151880839830945, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=200.0* ns, + high = 300.0*ns)}, + 'Xe2+': {'p': 0.001977311766106092, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.038992010108535* us, + scale = 0.065*us)}, + 'Uniform6': {'p': 0.0007010931680337559, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=800.0* ns, + high = 900.0*ns)}, + 'CH4': {'p': 0.00044852149134645914, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.001314546921497* us, + scale = 0.04*us)}, + 'Uniform1': {'p': 0.0019225985061687935, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=300.0* ns, + high = 400.0*ns)}, + 'Uniform7': {'p': 0.0006177508767339175, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=900.0* ns, + high = 1000.0*ns)}, + 'Uniform4': {'p': 0.0010338261325361646, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=600.0* ns, + high = 700.0*ns)}, + 'Ar': {'p': 0.0030849371794879157, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.602275802072991* us, + scale = 0.055*us)}, + 'Xe': {'p': 0.012307938683104397, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.8982131822366832* us, + scale = 0.09*us)}, + }, + 22: { + 'N2': {'p': 0.00012179585031320419, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.3321962075826972* us, + scale = 0.05*us)}, + 'Uniform2': {'p': 0.0016255290071069592, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=400.0* ns, + high = 500.0*ns)}, + 'He': {'p': 0.00027745688827447, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=0.5262010382419754* us, + scale = 0.04*us)}, + 'Ne': {'p': 0.00015684929015944343, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.1266187107558352* us, + scale = 0.045*us)}, + 'Uniform8': {'p': 0.010438795560990232, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=1000.0* ns, + high = 5000.0*ns)}, + 'Uniform5': {'p': 0.0008727712395614485, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=700.0* ns, + high = 800.0*ns)}, + 'Uniform3': {'p': 0.0013593605147151767, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=500.0* ns, + high = 600.0*ns)}, + 'Uniform0': {'p': 0.002989642530614846, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=200.0* ns, + high = 300.0*ns)}, + 'Xe2+': {'p': 8.614828436787613e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.0411839958090505* us, + scale = 0.065*us)}, + 'Uniform6': {'p': 0.0007462223804555339, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=800.0* ns, + high = 900.0*ns)}, + 'CH4': {'p': 7.545401458427771e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.0024020764839507* us, + scale = 0.04*us)}, + 'Uniform1': {'p': 0.0021043946429725327, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=300.0* ns, + high = 400.0*ns)}, + 'Uniform7': {'p': 0.0006862156444475651, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=900.0* ns, + high = 1000.0*ns)}, + 'Uniform4': {'p': 0.0011181453184851233, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=600.0* ns, + high = 700.0*ns)}, + 'Ar': {'p': 0.0010432854299999344, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.6039953388957235* us, + scale = 0.055*us)}, + 'Xe': {'p': 4.3965331332571267e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.9013250204352747* us, + scale = 0.09*us)}, + }, + 23: { + 'N2': {'p': 0.00018274968337511578, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.348819451079264* us, + scale = 0.05*us)}, + 'Uniform2': {'p': 0.0011884701631257528, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=400.0* ns, + high = 500.0*ns)}, + 'He': {'p': 0.00032966609550020887, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=0.5324840376778311* us, + scale = 0.04*us)}, + 'Ne': {'p': 0.00023709681143765018, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.1406678937840464* us, + scale = 0.045*us)}, + 'Uniform8': {'p': 0.013244574275195889, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=1000.0* ns, + high = 5000.0*ns)}, + 'Uniform5': {'p': 0.0006659016240629218, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=700.0* ns, + high = 800.0*ns)}, + 'Uniform3': {'p': 0.0009985538255006326, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=500.0* ns, + high = 600.0*ns)}, + 'Uniform0': {'p': 0.0023243848617514723, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=200.0* ns, + high = 300.0*ns)}, + 'Xe2+': {'p': 0.0005697490128753609, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.066511573826648* us, + scale = 0.065*us)}, + 'Uniform6': {'p': 0.000591846196812875, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=800.0* ns, + high = 900.0*ns)}, + 'CH4': {'p': 0.0001128748044375715, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.0149680753556622* us, + scale = 0.04*us)}, + 'Uniform1': {'p': 0.0015635250688759905, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=300.0* ns, + high = 400.0*ns)}, 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0.04*us)}, + 'Ne': {'p': 0.00012704683596384184, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.1266999044702304* us, + scale = 0.045*us)}, + 'Uniform8': {'p': 0.012476650813884979, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=1000.0* ns, + high = 5000.0*ns)}, + 'Uniform5': {'p': 0.001111496934124688, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=700.0* ns, + high = 800.0*ns)}, + 'Uniform3': {'p': 0.001531728776158934, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=500.0* ns, + high = 600.0*ns)}, + 'Uniform0': {'p': 0.003270641623274287, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=200.0* ns, + high = 300.0*ns)}, + 'Xe2+': {'p': 5.668243450694482e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.0413303701663175* us, + scale = 0.065*us)}, + 'Uniform6': {'p': 0.0009303737525967494, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=800.0* ns, + high = 900.0*ns)}, + 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4.8821116325298975e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.901013401971106* us, + scale = 0.09*us)}, + }, + 42: { + 'N2': {'p': 3.597261440935994e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.3316681786598712* us, + scale = 0.05*us)}, + 'Uniform2': {'p': 0.001145278111257997, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=400.0* ns, + high = 500.0*ns)}, + 'He': {'p': 0.0002828346807935925, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=0.5260014619423853* us, + scale = 0.04*us)}, + 'Ne': {'p': 4.4516110331582926e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.1261724455620117* us, + scale = 0.045*us)}, + 'Uniform8': {'p': 0.005736732682932676, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=1000.0* ns, + high = 5000.0*ns)}, + 'Uniform5': {'p': 0.0006938218004205299, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=700.0* ns, + high = 800.0*ns)}, + 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+ 'CH4': {'p': 5.3613422902328164e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.0022205871700858* us, + scale = 0.04*us)}, + 'Uniform1': {'p': 0.0013577333058178997, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=300.0* ns, + high = 400.0*ns)}, + 'Uniform7': {'p': 0.0004331680525883467, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=900.0* ns, + high = 1000.0*ns)}, + 'Uniform4': {'p': 0.0007150823425515822, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=600.0* ns, + high = 700.0*ns)}, + 'Ar': {'p': 0.000820888965232998, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.603708378826869* us, + scale = 0.055*us)}, + 'Xe': {'p': 5.25482555598978e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.9008057100652223* us, + scale = 0.09*us)}, + }, + 106: { + 'N2': {'p': 0.0003739928365130722, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.3318601196550133* us, + scale = 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800.0*ns)}, + 'Uniform3': {'p': 0.0007369491037963936, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=500.0* ns, + high = 600.0*ns)}, + 'Uniform0': {'p': 0.0016576498818656885, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=200.0* ns, + high = 300.0*ns)}, + 'Xe2+': {'p': 2.6805212526068082e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.0340485610563817* us, + scale = 0.065*us)}, + 'Uniform6': {'p': 0.00047511267999103286, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=800.0* ns, + high = 900.0*ns)}, + 'CH4': {'p': 4.6229279573943505e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=0.9988619091221562* us, + scale = 0.04*us)}, + 'Uniform1': {'p': 0.0011351424782778397, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=300.0* ns, + high = 400.0*ns)}, + 'Uniform7': {'p': 0.0003849850088888909, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=900.0* ns, + high = 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0.065*us)}, + 'Uniform6': {'p': 6.174859745469492e-05, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=800.0* ns, + high = 900.0*ns)}, + 'CH4': {'p': 4.4526199525154164e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.0022205871700858* us, + scale = 0.04*us)}, + 'Uniform1': {'p': 0.00015878210774064407, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=300.0* ns, + high = 400.0*ns)}, + 'Uniform7': {'p': 5.810809057527979e-05, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=900.0* ns, + high = 1000.0*ns)}, + 'Uniform4': {'p': 9.843370523957035e-05, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=600.0* ns, + high = 700.0*ns)}, + 'Ar': {'p': 0.0005911623094188706, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.603708378826869* us, + scale = 0.055*us)}, + 'Xe': {'p': 1.4702047013022599e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.9008057100652223* us, + scale = 0.09*us)}, + }, + 246: { + 'N2': {'p': 0.00011950532201381393, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.3321001576182803* us, + scale = 0.05*us)}, + 'Uniform2': {'p': 0.0001610111022676287, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=400.0* ns, + high = 500.0*ns)}, + 'He': {'p': 0.00017604715851052012, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=0.5261647347448649* us, + scale = 0.04*us)}, + 'Ne': {'p': 0.00010587889604369358, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.126537533846514* us, + scale = 0.045*us)}, + 'Uniform8': {'p': 0.0012830767993933993, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=1000.0* ns, + high = 5000.0*ns)}, + 'Uniform5': {'p': 0.00010979453569027988, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=700.0* ns, + high = 800.0*ns)}, + 'Uniform3': {'p': 0.0001498906856713236, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=500.0* ns, + high = 600.0*ns)}, + 'Uniform0': {'p': 0.000496503107187143, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=200.0* ns, + high = 300.0*ns)}, + 'Xe2+': {'p': 5.027681306216811e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.041037651747625* us, + scale = 0.065*us)}, + 'Uniform6': {'p': 9.319222358875397e-05, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=800.0* ns, + high = 900.0*ns)}, + 'CH4': {'p': 5.998759938570214e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.0023294694897298* us, + scale = 0.04*us)}, + 'Uniform1': {'p': 0.00021019153622875265, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=300.0* ns, + high = 400.0*ns)}, + 'Uniform7': {'p': 8.865008159871386e-05, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=900.0* ns, + high = 1000.0*ns)}, + 'Uniform4': {'p': 0.00012858960599389414, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=600.0* ns, + high = 700.0*ns)}, + 'Ar': {'p': 0.000968415997393724, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.6038805370508253* us, + scale = 0.055*us)}, + 'Xe': {'p': 2.3493837879517806e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.901117264043263* us, + scale = 0.09*us)}, + }, + 247: { + 'N2': {'p': 2.639192889863458e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.338287729612839* us, + scale = 0.05*us)}, + 'Uniform2': {'p': 0.00018796918248916407, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=400.0* ns, + high = 500.0*ns)}, + 'He': {'p': 0.00016348333734431976, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=0.5285034186099697* us, + scale = 0.04*us)}, + 'Ne': {'p': 3.6802078630873776e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.1317669784774456* us, + scale = 0.045*us)}, + 'Uniform8': {'p': 0.0010021601890120408, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=1000.0* ns, + high = 5000.0*ns)}, + 'Uniform5': {'p': 0.00011524475619070433, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=700.0* ns, + high = 800.0*ns)}, + 'Uniform3': {'p': 0.00017623943631199314, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=500.0* ns, + high = 600.0*ns)}, + 'Uniform0': {'p': 0.0006427900905089666, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=200.0* ns, + high = 300.0*ns)}, + 'Xe2+': {'p': 1.3489208103746562e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.0504651869012824* us, + scale = 0.065*us)}, + 'Uniform6': {'p': 0.00010028932981481141, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=800.0* ns, + high = 900.0*ns)}, + 'CH4': {'p': 2.6538550725849214e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.0070068372199394* us, + scale = 0.04*us)}, + 'Uniform1': {'p': 0.00026949091842050197, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=300.0* ns, + high = 400.0*ns)}, + 'Uniform7': {'p': 9.14920201819332e-05, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=900.0* ns, + high = 1000.0*ns)}, + 'Uniform4': {'p': 0.0001372380302728998, + 'time_distribution': 'uniform', + 'time_parameters': dict(low=600.0* ns, + high = 700.0*ns)}, + 'Ar': {'p': 2.859125630685413e-05, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=1.611276111684594* us, + scale = 0.055*us)}, + 'Xe': {'p': 9.237175114522102e-06, + 'time_distribution': 'normal', + 'time_parameters': dict(loc=2.914501002816199* us, + scale = 0.09*us)}, + }, + 248: { +======= each_pmt_afterpulse_types = { 0: { 'Ne': {'p': 0.011114743338516945, @@ -7777,7 +22486,6 @@ each_pmt_afterpulse_types = { } - # Example S2 afterpulse model from Qing # values extracted from Kevin's note: # xenon:xenon1t:micheneau:single_electron_study_xenon1t @@ -7927,12 +22635,539 @@ channels_bottom = list(range(127, 247 + 1)) # PMT gains # The correct TPC PMT gains are now fetched from the runs db, these are just some placeholder values -gains = [2.5e6] * 248 + \ - [2e6] * 6 +\ # 248-253 Placeholders for 1-inch XENON100 PMT - [2.5e6 / 31.25] +\ # 254, Acquisition monitor sum waveform. Signal is attenuated by 31.25 in NIM FAN, see #392 - [1e5] * 5 # 256-259 "Digital inputs" on acquisition monitor. Numbers are just to make plots look reasonable. +# @ 2017-02-14 The default gains now come from PMT WG. +# It is the average values for the first science run +# Only for waveform simulation usage +gains = [ + 2454700.0, # PMT ID = 0 + 0.0, # PMT ID = 1 + 2220960.0, # PMT ID = 2 + 2155720.0, # PMT ID = 3 + 3914230.0, # PMT ID = 4 + 2193190.0, # PMT ID = 5 + 4794680.0, # PMT ID = 6 + 2306410.0, # PMT ID = 7 + 2302830.0, # PMT ID = 8 + 3568740.0, # PMT ID = 9 + 2431830.0, # PMT ID = 10 + 3051300.0, # PMT ID = 11 + 0.0, # PMT ID = 12 + 3106890.0, # PMT ID = 13 + 2162410.0, # PMT ID = 14 + 2447030.0, # PMT ID = 15 + 2680920.0, # PMT ID = 16 + 2142420.0, # PMT ID = 17 + 2399060.0, # PMT ID = 18 + 2299720.0, # PMT ID = 19 + 2393640.0, # PMT ID = 20 + 2240190.0, # PMT ID = 21 + 4351390.0, # PMT ID = 22 + 2360750.0, # PMT ID = 23 + 3167230.0, # PMT ID = 24 + 2316990.0, # PMT ID = 25 + 0.0, # PMT ID = 26 + 0.0, # PMT ID = 27 + 2369150.0, # PMT ID = 28 + 2360410.0, # PMT ID = 29 + 4065970.0, # PMT ID = 30 + 2293450.0, # PMT ID = 31 + 4155640.0, # PMT ID = 32 + 2455290.0, # PMT ID = 33 + 0.0, # PMT ID = 34 + 3181110.0, # PMT ID = 35 + 2343640.0, # PMT ID = 36 + 2470630.0, # PMT ID = 37 + 3798760.0, # PMT ID = 38 + 3369460.0, # PMT ID = 39 + 2450890.0, # PMT ID = 40 + 3178970.0, # PMT ID = 41 + 3736900.0, # PMT ID = 42 + 2350790.0, # PMT ID = 43 + 2795680.0, # PMT ID = 44 + 2641520.0, # PMT ID = 45 + 2262460.0, # PMT ID = 46 + 3631590.0, # PMT ID = 47 + 2314930.0, # PMT ID = 48 + 3264430.0, # PMT ID = 49 + 3760810.0, # PMT ID = 50 + 0.0, # PMT ID = 51 + 2409370.0, # PMT ID = 52 + 2139890.0, # PMT ID = 53 + 3652190.0, # PMT ID = 54 + 1972600.0, # PMT ID = 55 + 4568780.0, # PMT ID = 56 + 3448980.0, # PMT ID = 57 + 2104010.0, # PMT ID = 58 + 2270160.0, # PMT ID = 59 + 2844820.0, # PMT ID = 60 + 2129320.0, # PMT ID = 61 + 0.0, # PMT ID = 62 + 3808380.0, # PMT ID = 63 + 3544490.0, # PMT ID = 64 + 0.0, # PMT ID = 65 + 2288970.0, # PMT ID = 66 + 2391430.0, # PMT ID = 67 + 4828820.0, # PMT ID = 68 + 3072220.0, # PMT ID = 69 + 3779430.0, # PMT ID = 70 + 2222680.0, # PMT ID = 71 + 2232660.0, # PMT ID = 72 + 0.0, # PMT ID = 73 + 4812910.0, # PMT ID = 74 + 4664870.0, # PMT ID = 75 + 2921810.0, # PMT ID = 76 + 3367480.0, # PMT ID = 77 + 1913240.0, # PMT ID = 78 + 0.0, # PMT ID = 79 + 3377140.0, # PMT ID = 80 + 4191370.0, # PMT ID = 81 + 4439910.0, # PMT ID = 82 + 2110200.0, # PMT ID = 83 + 2550060.0, # PMT ID = 84 + 2188750.0, # PMT ID = 85 + 0.0, # PMT ID = 86 + 2488540.0, # PMT ID = 87 + 0.0, # PMT ID = 88 + 2653900.0, # PMT ID = 89 + 2332770.0, # PMT ID = 90 + 0.0, # PMT ID = 91 + 3498370.0, # PMT ID = 92 + 2312630.0, # PMT ID = 93 + 2959870.0, # PMT ID = 94 + 4258430.0, # PMT ID = 95 + 4458110.0, # PMT ID = 96 + 2982780.0, # PMT ID = 97 + 2103810.0, # PMT ID = 98 + 2251460.0, # PMT ID = 99 + 2255500.0, # PMT ID = 100 + 3081240.0, # PMT ID = 101 + 0.0, # PMT ID = 102 + 2403450.0, # PMT ID = 103 + 3415100.0, # PMT ID = 104 + 3740300.0, # PMT ID = 105 + 3904240.0, # PMT ID = 106 + 2313870.0, # PMT ID = 107 + 4807940.0, # PMT ID = 108 + 2286720.0, # PMT ID = 109 + 2402270.0, # PMT ID = 110 + 2373820.0, # PMT ID = 111 + 2228830.0, # PMT ID = 112 + 2858710.0, # PMT ID = 113 + 2916730.0, # PMT ID = 114 + 1781090.0, # PMT ID = 115 + 3648120.0, # PMT ID = 116 + 2187960.0, # PMT ID = 117 + 0.0, # PMT ID = 118 + 2264870.0, # PMT ID = 119 + 2452100.0, # PMT ID = 120 + 4718690.0, # PMT ID = 121 + 2300960.0, # PMT ID = 122 + 2306600.0, # PMT ID = 123 + 2213380.0, # PMT ID = 124 + 2221760.0, # PMT ID = 125 + 4896670.0, # PMT ID = 126 + 4770270.0, # PMT ID = 127 + 2310130.0, # PMT ID = 128 + 2685350.0, # PMT ID = 129 + 0.0, # PMT ID = 130 + 3365590.0, # PMT ID = 131 + 4624650.0, # PMT ID = 132 + 3286860.0, # PMT ID = 133 + 0.0, # PMT ID = 134 + 0.0, # PMT ID = 135 + 3889670.0, # PMT ID = 136 + 0.0, # PMT ID = 137 + 4449840.0, # PMT ID = 138 + 0.0, # PMT ID = 139 + 4762350.0, # PMT ID = 140 + 3208830.0, # PMT ID = 141 + 2236040.0, # PMT ID = 142 + 3357250.0, # PMT ID = 143 + 2659320.0, # PMT ID = 144 + 3396690.0, # PMT ID = 145 + 3991870.0, # PMT ID = 146 + 4898720.0, # PMT ID = 147 + 0.0, # PMT ID = 148 + 2353690.0, # PMT ID = 149 + 0.0, # PMT ID = 150 + 4052570.0, # PMT ID = 151 + 0.0, # PMT ID = 152 + 3628540.0, # PMT ID = 153 + 2743550.0, # PMT ID = 154 + 2358020.0, # PMT ID = 155 + 3237420.0, # PMT ID = 156 + 2287810.0, # PMT ID = 157 + 4468640.0, # PMT ID = 158 + 4120150.0, # PMT ID = 159 + 2291580.0, # PMT ID = 160 + 2351640.0, # PMT ID = 161 + 0.0, # PMT ID = 162 + 2218320.0, # PMT ID = 163 + 2282280.0, # PMT ID = 164 + 2583050.0, # PMT ID = 165 + 2360800.0, # PMT ID = 166 + 0.0, # PMT ID = 167 + 2279370.0, # PMT ID = 168 + 4541610.0, # PMT ID = 169 + 4793430.0, # PMT ID = 170 + 3299140.0, # PMT ID = 171 + 3995930.0, # PMT ID = 172 + 2331190.0, # PMT ID = 173 + 2216930.0, # PMT ID = 174 + 4323330.0, # PMT ID = 175 + 2757670.0, # PMT ID = 176 + 2936360.0, # PMT ID = 177 + 0.0, # PMT ID = 178 + 2342170.0, # PMT ID = 179 + 4784790.0, # PMT ID = 180 + 4817250.0, # PMT ID = 181 + 3261000.0, # PMT ID = 182 + 0.0, # PMT ID = 183 + 2239070.0, # PMT ID = 184 + 4246190.0, # PMT ID = 185 + 4003630.0, # PMT ID = 186 + 4074060.0, # PMT ID = 187 + 2262390.0, # PMT ID = 188 + 2271890.0, # PMT ID = 189 + 4770520.0, # PMT ID = 190 + 2658560.0, # PMT ID = 191 + 3535460.0, # PMT ID = 192 + 4630830.0, # PMT ID = 193 + 3372580.0, # PMT ID = 194 + 2256000.0, # PMT ID = 195 + 2253920.0, # PMT ID = 196 + 2234930.0, # PMT ID = 197 + 0.0, # PMT ID = 198 + 3268330.0, # PMT ID = 199 + 2168200.0, # PMT ID = 200 + 3443220.0, # PMT ID = 201 + 2170770.0, # PMT ID = 202 + 0.0, # PMT ID = 203 + 3892380.0, # PMT ID = 204 + 1904620.0, # PMT ID = 205 + 0.0, # PMT ID = 206 + 3810420.0, # PMT ID = 207 + 2432080.0, # PMT ID = 208 + 2031360.0, # PMT ID = 209 + 3356590.0, # PMT ID = 210 + 3857000.0, # PMT ID = 211 + 4256180.0, # PMT ID = 212 + 0.0, # PMT ID = 213 + 0.0, # PMT ID = 214 + 4834730.0, # PMT ID = 215 + 4109580.0, # PMT ID = 216 + 2037580.0, # PMT ID = 217 + 4711720.0, # PMT ID = 218 + 4705220.0, # PMT ID = 219 + 2352110.0, # PMT ID = 220 + 4788170.0, # PMT ID = 221 + 3412780.0, # PMT ID = 222 + 4755180.0, # PMT ID = 223 + 3188370.0, # PMT ID = 224 + 2385220.0, # PMT ID = 225 + 4619810.0, # PMT ID = 226 + 2764010.0, # PMT ID = 227 + 2653900.0, # PMT ID = 228 + 4630550.0, # PMT ID = 229 + 2230830.0, # PMT ID = 230 + 3732750.0, # PMT ID = 231 + 2296410.0, # PMT ID = 232 + 2553580.0, # PMT ID = 233 + 0.0, # PMT ID = 234 + 4664230.0, # PMT ID = 235 + 2295320.0, # PMT ID = 236 + 2274810.0, # PMT ID = 237 + 4755530.0, # PMT ID = 238 + 0.0, # PMT ID = 239 + 2328320.0, # PMT ID = 240 + 4243100.0, # PMT ID = 241 + 4610070.0, # PMT ID = 242 + 4307860.0, # PMT ID = 243 + 0.0, # PMT ID = 244 + 3981150.0, # PMT ID = 245 + 3444510.0, # PMT ID = 246 + 4587680.0, # PMT ID = 247 + 2e6, # PMT ID = 248 + 2e6, # PMT ID = 249 + 2e6, # PMT ID = 250 + 2e6, # PMT ID = 251 + 2e6, # PMT ID = 252 + 2e6, # PMT ID = 253 + 2.5e6 / 31.25, # PMT ID = 254 + 1e5, # PMT ID = 255 + 1e5, # PMT ID = 256 + 1e5, # PMT ID = 257 + 1e5, # PMT ID = 258 + 1e5, # PMT ID = 259 + ] -gain_sigmas = [1e6] * 260 # PLACEHOLDER +# @2017-02-14 +# Placeholder +# The gain_sigma is currently 0.35*gain +# the method comes from the reference: +# https://arxiv.org/pdf/1609.01654.pdf +gain_sigmas = [ + 999568.5682, # PMT ID = 0 + 0.0, # PMT ID = 1 + 922435.75872, # PMT ID = 2 + 873204.5660799999, # PMT ID = 3 + 1659308.63891, # PMT ID = 4 + 949916.6459900001, # PMT ID = 5 + 2093318.93056, # PMT ID = 6 + 971466.81123, # PMT ID = 7 + 985542.1550999999, # PMT ID = 8 + 1517456.79792, # PMT ID = 9 + 1006135.6168800001, # PMT ID = 10 + 1304943.3684, # PMT ID = 11 + 0.0, # PMT ID = 12 + 1392094.88163, # PMT ID = 13 + 960624.69358, # PMT ID = 14 + 1074950.91464, # PMT ID = 15 + 1182368.82852, # PMT ID = 16 + 981020.54526, # PMT ID = 17 + 1024069.9487800001, # PMT ID = 18 + 935229.43212, # PMT ID = 19 + 993037.4586, # PMT ID = 20 + 940436.2423800001, # PMT ID = 21 + 1843331.48041, # PMT ID = 22 + 970560.983, # PMT ID = 23 + 1392159.1137299999, # PMT ID = 24 + 977554.29993, # PMT ID = 25 + 0.0, # PMT ID = 26 + 0.0, # PMT ID = 27 + 1015010.1961999999, # PMT ID = 28 + 1010973.0446399999, # PMT ID = 29 + 1632966.73946, # PMT ID = 30 + 936213.8114, # PMT ID = 31 + 1763242.20764, # PMT ID = 32 + 987053.5881899999, # PMT ID = 33 + 0.0, # PMT ID = 34 + 1407135.3785099997, # PMT ID = 35 + 960913.49276, # PMT ID = 36 + 948494.6220399999, # PMT ID = 37 + 1687671.3064400002, # PMT ID = 38 + 1379692.7862, # PMT ID = 39 + 944340.1714500001, # PMT ID = 40 + 1350401.02424, # PMT ID = 41 + 1643720.3078, # PMT ID = 42 + 962523.9131299999, # PMT ID = 43 + 1180344.48304, # PMT ID = 44 + 1066661.62512, # PMT ID = 45 + 943310.0724000001, # PMT ID = 46 + 1509553.91007, # PMT ID = 47 + 982352.12015, # PMT ID = 48 + 1399239.1597600002, # PMT ID = 49 + 1639577.7708400001, # PMT ID = 50 + 0.0, # PMT ID = 51 + 1030856.1826099999, # PMT ID = 52 + 870699.8420999999, # PMT ID = 53 + 1474166.3194100002, # PMT ID = 54 + 804566.3346000001, # PMT ID = 55 + 1922994.93322, # PMT ID = 56 + 1502296.36146, # PMT ID = 57 + 874603.2928399999, # PMT ID = 58 + 950663.5523999999, # PMT ID = 59 + 1193245.5248999998, # PMT ID = 60 + 924137.6559199999, # PMT ID = 61 + 0.0, # PMT ID = 62 + 1590318.55392, # PMT ID = 63 + 1555616.40467, # PMT ID = 64 + 0.0, # PMT ID = 65 + 949641.00669, # PMT ID = 66 + 992357.35852, # PMT ID = 67 + 2081216.59118, # PMT ID = 68 + 1261011.13232, # PMT ID = 69 + 1506099.0755699999, # PMT ID = 70 + 892977.24876, # PMT ID = 71 + 931008.0567000001, # PMT ID = 72 + 0.0, # PMT ID = 73 + 2011108.13387, # PMT ID = 74 + 2024306.34189, # PMT ID = 75 + 1271717.8025, # PMT ID = 76 + 1486395.56956, # PMT ID = 77 + 806254.64192, # PMT ID = 78 + 0.0, # PMT ID = 79 + 1454510.5580199999, # PMT ID = 80 + 1846730.19611, # PMT ID = 81 + 1909214.57892, # PMT ID = 82 + 871343.7840000001, # PMT ID = 83 + 1102426.6388400001, # PMT ID = 84 + 951381.77375, # PMT ID = 85 + 0.0, # PMT ID = 86 + 973835.38112, # PMT ID = 87 + 0.0, # PMT ID = 88 + 1087899.9575, # PMT ID = 89 + 984930.4855499999, # PMT ID = 90 + 0.0, # PMT ID = 91 + 1431505.01704, # PMT ID = 92 + 893207.0848999999, # PMT ID = 93 + 1238525.04293, # PMT ID = 94 + 1784639.87812, # PMT ID = 95 + 1967350.56867, # PMT ID = 96 + 1232714.3700599999, # PMT ID = 97 + 893515.45653, # PMT ID = 98 + 945268.7266199999, # PMT ID = 99 + 950235.3835, # PMT ID = 100 + 1306633.71564, # PMT ID = 101 + 0.0, # PMT ID = 102 + 992821.9328999999, # PMT ID = 103 + 1544332.1257, # PMT ID = 104 + 1672665.9003, # PMT ID = 105 + 1632573.5729599998, # PMT ID = 106 + 925175.46693, # PMT ID = 107 + 2060370.5679, # PMT ID = 108 + 928813.0694400001, # PMT ID = 109 + 1035082.89079, # PMT ID = 110 + 988843.20684, # PMT ID = 111 + 966668.0881299999, # PMT ID = 112 + 1112804.32428, # PMT ID = 113 + 1224921.59772, # PMT ID = 114 + 741214.85222, # PMT ID = 115 + 1608102.2403600002, # PMT ID = 116 + 857262.41964, # PMT ID = 117 + 0.0, # PMT ID = 118 + 969232.9975399999, # PMT ID = 119 + 1028977.1750999999, # PMT ID = 120 + 2058141.57992, # PMT ID = 121 + 968984.87712, # PMT ID = 122 + 952390.5267999999, # PMT ID = 123 + 926414.6257600001, # PMT ID = 124 + 987370.13984, # PMT ID = 125 + 2061468.6899800003, # PMT ID = 126 + 2081297.4226199999, # PMT ID = 127 + 1003617.4974600001, # PMT ID = 128 + 1130978.1181, # PMT ID = 129 + 0.0, # PMT ID = 130 + 1451437.61222, # PMT ID = 131 + 2121835.6665000003, # PMT ID = 132 + 1414730.2812, # PMT ID = 133 + 0.0, # PMT ID = 134 + 0.0, # PMT ID = 135 + 1652786.9073899998, # PMT ID = 136 + 0.0, # PMT ID = 137 + 1987863.67368, # PMT ID = 138 + 0.0, # PMT ID = 139 + 2061354.6234, # PMT ID = 140 + 1360691.52618, # PMT ID = 141 + 885087.24112, # PMT ID = 142 + 1437859.81625, # PMT ID = 143 + 1105572.4002, # PMT ID = 144 + 1344854.86839, # PMT ID = 145 + 1672393.9365, # PMT ID = 146 + 2062684.43552, # PMT ID = 147 + 0.0, # PMT ID = 148 + 981867.67409, # PMT ID = 149 + 0.0, # PMT ID = 150 + 1703060.12194, # PMT ID = 151 + 0.0, # PMT ID = 152 + 1454797.79928, # PMT ID = 153 + 1290445.2038, # PMT ID = 154 + 958940.70944, # PMT ID = 155 + 1452173.87778, # PMT ID = 156 + 987146.54661, # PMT ID = 157 + 1892513.7263999998, # PMT ID = 158 + 1775401.47605, # PMT ID = 159 + 964131.8702400001, # PMT ID = 160 + 1000359.43632, # PMT ID = 161 + 0.0, # PMT ID = 162 + 912781.00368, # PMT ID = 163 + 952943.1911999999, # PMT ID = 164 + 1073350.2648, # PMT ID = 165 + 1023267.5128, # PMT ID = 166 + 0.0, # PMT ID = 167 + 969037.6855799999, # PMT ID = 168 + 1961398.73553, # PMT ID = 169 + 2045677.7408099996, # PMT ID = 170 + 1404589.06016, # PMT ID = 171 + 1680264.58942, # PMT ID = 172 + 962030.82682, # PMT ID = 173 + 913173.4193699999, # PMT ID = 174 + 1808284.65246, # PMT ID = 175 + 1140335.1523799999, # PMT ID = 176 + 1229151.4869199998, # PMT ID = 177 + 0.0, # PMT ID = 178 + 969716.9342500002, # PMT ID = 179 + 2031899.3518200002, # PMT ID = 180 + 2087964.75375, # PMT ID = 181 + 1360287.0180000002, # PMT ID = 182 + 0.0, # PMT ID = 183 + 1011786.47346, # PMT ID = 184 + 1843020.55379, # PMT ID = 185 + 1690160.42991, # PMT ID = 186 + 1715000.0013599999, # PMT ID = 187 + 965049.6031799999, # PMT ID = 188 + 980395.50737, # PMT ID = 189 + 2073778.43764, # PMT ID = 190 + 1133745.57056, # PMT ID = 191 + 1526717.6918000001, # PMT ID = 192 + 1943526.9351899999, # PMT ID = 193 + 1473368.9068600002, # PMT ID = 194 + 966793.0080000001, # PMT ID = 195 + 949186.5678399999, # PMT ID = 196 + 960970.73154, # PMT ID = 197 + 0.0, # PMT ID = 198 + 1429289.7339499998, # PMT ID = 199 + 854765.1496000001, # PMT ID = 200 + 1445752.98648, # PMT ID = 201 + 897287.7795000001, # PMT ID = 202 + 0.0, # PMT ID = 203 + 1592154.68472, # PMT ID = 204 + 816638.20354, # PMT ID = 205 + 0.0, # PMT ID = 206 + 1711892.15172, # PMT ID = 207 + 1001401.64376, # PMT ID = 208 + 853668.8832, # PMT ID = 209 + 1369925.0767, # PMT ID = 210 + 1689269.575, # PMT ID = 211 + 1830876.69442, # PMT ID = 212 + 0.0, # PMT ID = 213 + 0.0, # PMT ID = 214 + 2021811.5650499999, # PMT ID = 215 + 1722900.3191999998, # PMT ID = 216 + 881803.4966, # PMT ID = 217 + 2029318.9571200002, # PMT ID = 218 + 1981688.0969599998, # PMT ID = 219 + 975121.29903, # PMT ID = 220 + 1984164.97813, # PMT ID = 221 + 1419713.06722, # PMT ID = 222 + 1990518.3480000002, # PMT ID = 223 + 1316984.92383, # PMT ID = 224 + 906290.5764199999, # PMT ID = 225 + 1978179.54295, # PMT ID = 226 + 1151652.4065999999, # PMT ID = 227 + 1134114.9721, # PMT ID = 228 + 1923548.9922, # PMT ID = 229 + 976630.60404, # PMT ID = 230 + 1605541.62825, # PMT ID = 231 + 981331.7745299999, # PMT ID = 232 + 1017070.4853599999, # PMT ID = 233 + 0.0, # PMT ID = 234 + 1983538.43518, # PMT ID = 235 + 939577.7654, # PMT ID = 236 + 976041.35265, # PMT ID = 237 + 1931986.37333, # PMT ID = 238 + 0.0, # PMT ID = 239 + 983123.8067200001, # PMT ID = 240 + 1853352.1352000001, # PMT ID = 241 + 1990946.32083, # PMT ID = 242 + 1894769.1424, # PMT ID = 243 + 0.0, # PMT ID = 244 + 1790729.2323000003, # PMT ID = 245 + 1467867.60297, # PMT ID = 246 + 1965522.6807999997, # PMT ID = 247 + 700000.0, # PMT ID = 248 + 700000.0, # PMT ID = 249 + 700000.0, # PMT ID = 250 + 700000.0, # PMT ID = 251 + 700000.0, # PMT ID = 252 + 700000.0, # PMT ID = 253 + 28000.0, # PMT ID = 254 + 35000.0, # PMT ID = 255 + 35000.0, # PMT ID = 256 + 35000.0, # PMT ID = 257 + 35000.0, # PMT ID = 258 + 35000.0, # PMT ID = 259 + ] # QES from https://xecluster.lngs.infn.it/xenon1tpmts/daniel_test/tpc_view_datasheets.php # (Hamamatsu datasheets) quantum_efficiencies = [