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pdeperio/XENON100_light.ini

Created September 11, 2015 13:53
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XENON100 ROOT output config file for pax 3.3.0
Raw
XENON100_light.ini
##
# XENON100-specific configuration settings
##
# This is just for setting up pax
[pax]
parent_configuration = "XENON100"
# Plugin groups: plugins from these groups will be loaded (in order)
plugin_group_names = ['input', 'dsp', 'transform', 'output']
input = 'XED.ReadXED'
output = ['Table.TableWriter']
dsp = [
'CheckPulses.CheckBounds',
# Find individual hits
'HitFinder.FindHits',
# Combine hits into clusters = peaks
#'Cluster.MeanShift',
'Cluster.GapSize',
# Compute properties for each peak
'ComputePeakProperties.BasicProperties',
'ComputePeakProperties.HitpatternSpread',
# Classify the clusters based on the properties
'ClassifyPeaks.AdHocClassification',
# Make sum waveform for plotting - disable for speed in bulk processing
#'SumWaveform.SumWaveform',
]
transform = [
'PosSimple.PosRecWeightedSum',
'NeuralNet.PosRecNeuralNet'
]
[Table.TableWriter]
output_format = 'root' # hdf5, csv, numpy, html, json, root
append_data = False
overwrite_data = True
string_data_length = 32
# Convert data to numpy records every n events
# These take less memory, but conversion takes some time
buffer_size = 50
# Write to file every time a chunk is converted to records
# If false, or output format does not support it, will write all at end
write_in_chunks = False
# Fields to leave out of the output.
# If you dump to e.g. json, you may want to ignore area_per_channel and does_channel_contribute
fields_to_ignore = ['hits',
'pulses',
'sum_waveforms',
'channel_waveforms',
'all_hits',
'raw_data',
'area_per_channel', # Useful, but takes a LOT of space
'does_channel_have_noise', # Takes little disk space, but a lot of memory
'does_channel_contribute', # Same here
]
[HitFinder.FindHits]
# Minimum sigma's above noise level for an excess in a channel to be regarded as a hit:
peak_minimum_sigma = 6 # 8 sigma gives too many missed hits
# Initial guess for the digitizer baseline during this pulse; will be refined during hitfinding.
baseline_guess = 16000 # ADC-counts
# Initial guess for the noise_sigma; will be refined during hitfinding.
# If set too high, peaks may not get found; if set too low, spurious peaks may be found in noise.
noise_sigma_guess = 10 # ADC-counts -- not pe/bin anymore!!
# Output peakfinder diagnostic plots to directory
# Warning: this will produce LOTS of plots, processing will be EXTREMELY SLOW
# That's why they're called diagnostic plots...
make_diagnostic_plots = 'never' #'always', 'never' (default), or 'no peaks'
make_diagnostic_plots_in = 'diagnostic_plots'
# The algorithm refines its noise_sigma by excluding all samples part of a peak.
# Then peakfinding is repeated, then noise_sigma finding, etc.
# Usually this process is self-stabilizing, so it rarely takes more than 3 or 4 passes.
# Occasionally we never settle on peaks and the noise_sigma remains oscillatory.
# Usually this is one or two samples bouncing in an and of a peak boundary.
# To protect against infinite loops, if more than the below number of passes have been done,
# no new pass is started.
max_passes = 20
# Max hits to look for in each pulse: rest will be ignored
# After large peaks the zero-length encoding can fail, making a huge pulse with many hits
# If set too low, you risk missing some hits in such events.
# If set too high, it will degrade performance (though the effect appears to be small)
# Don't set to infinity though: we need to allocate memory for this...
max_hits_per_pulse = 500
[Cluster]
# Suspicious channel rejection settings
penalty_per_noise_pulse = 1 # "pe" equivalent penalty
penalty_per_lone_hit = 1 # "pe" equivalent penalty
# Threshold to mark a suspicious channel
penalty_geq_this_is_suspicious = 3 # "pe" equivalent penalty
# If the ratio of noise channels / contributing channels is larger than this, classify peak as 'noise'
# noise channel = a channel in the same detector which shows data, but doesn't contribute to the peak
# (or only hits rejected by the suspicious channel algorithm)
max_noise_channels_over_contributing_channels = 2
[Cluster.MeanShift]
s2_size = 20
# If spe peaks are separated by less than this, they will be clustered together
s2_width = 1.0 * us
p_value = 0.999
cluster_all = True
[Cluster.HitDifference]
max_difference = 800 * ns
[Cluster.GapSize]
# If there is a gap between hits larger than this, it will make a new cluster
large_gap_threshold = 450 * ns
# If the area in a cluster is larger than this, it is certainly not a single electron, so we can use...
transition_point = 50 #pe
# ... a tighter gap threshold:
small_gap_threshold = 100 * ns
# Global settings, passed to every plugin
[DEFAULT]
tpc_name = "XENON100"
# Time in the event at which trigger occurs. Set to None or leave out if there is no trigger
trigger_time_in_event = 209 * us # Jelle: I have no idea if this is right. Anybody?
pmt_0_is_fake = True
# Detector specification
# PlotChannelWaveform2D expects the detector names' lexical order to be the same as the channel order
channels_in_detector = {
'tpc': list(range(0, 178+1)),
'veto': list(range(179, 242+1)),
}
n_channels = 242 + 1 # +1 for the fake pmt 0
# PMT numbers for tpc, specified as lists
# Remember python range does not include endpoint!
# PMT 0 does not exist, its gain is set to 0 later
channels_top = list(range(0, 98 + 1))
channels_bottom = list(range(99, 178 + 1))
# PMT locations taken from Marc Schumann's pmtpattern code. Agrees also
# with the top PMT locations used by xerawdp.
# Note: don't forget the units...
pmt_locations = [ {'x' : 0 * mm, 'y' : 0 * mm},
{'x' : -166.84 * mm, 'y' : 0.00 * mm}, {'x' : -163.19 * mm, 'y' : 34.69 * mm},
{'x' : -152.42 * mm, 'y' : 67.86 * mm}, {'x' : -134.98 * mm, 'y' : 98.07 * mm},
{'x' : -111.64 * mm, 'y' : 123.99 * mm}, {'x' : -83.42 * mm, 'y' : 144.49 * mm},
{'x' : -51.56 * mm, 'y' : 158.67 * mm}, {'x' : -17.44 * mm, 'y' : 165.93 * mm},
{'x' : 17.44 * mm, 'y' : 165.93 * mm}, {'x' : 51.56 * mm, 'y' : 158.67 * mm},
{'x' : 83.42 * mm, 'y' : 144.49 * mm}, {'x' : 111.64 * mm, 'y' : 123.99 * mm},
{'x' : 134.98 * mm, 'y' : 98.07 * mm}, {'x' : 152.42 * mm, 'y' : 67.86 * mm},
{'x' : 163.19 * mm, 'y' : 34.69 * mm}, {'x' : 166.84 * mm, 'y' : 0.00 * mm},
{'x' : 163.19 * mm, 'y' : -34.69 * mm}, {'x' : 152.42 * mm, 'y' : -67.86 * mm},
{'x' : 134.98 * mm, 'y' : -98.07 * mm}, {'x' : 111.64 * mm, 'y' : -123.99 * mm},
{'x' : 83.42 * mm, 'y' : -144.49 * mm}, {'x' : 51.56 * mm, 'y' : -158.67 * mm},
{'x' : 17.44 * mm, 'y' : -165.93 * mm}, {'x' : -17.44 * mm, 'y' : -165.93 * mm},
{'x' : -51.56 * mm, 'y' : -158.67 * mm}, {'x' : -83.42 * mm, 'y' : -144.49 * mm},
{'x' : -111.64 * mm, 'y' : -123.99 * mm}, {'x' : -134.98 * mm, 'y' : -98.07 * mm},
{'x' : -152.42 * mm, 'y' : -67.86 * mm}, {'x' : -163.19 * mm, 'y' : -34.69 * mm},
{'x' : -136.53 * mm, 'y' : 0.00 * mm}, {'x' : -131.88 * mm, 'y' : 35.34 * mm},
{'x' : -118.24 * mm, 'y' : 68.27 * mm}, {'x' : -96.54 * mm, 'y' : 96.54 * mm},
{'x' : -68.27 * mm, 'y' : 118.24 * mm}, {'x' : -35.34 * mm, 'y' : 131.88 * mm},
{'x' : 0.00 * mm, 'y' : 136.53 * mm}, {'x' : 35.34 * mm, 'y' : 131.88 * mm},
{'x' : 68.27 * mm, 'y' : 118.24 * mm}, {'x' : 96.54 * mm, 'y' : 96.54 * mm},
{'x' : 118.24 * mm, 'y' : 68.27 * mm}, {'x' : 131.88 * mm, 'y' : 35.34 * mm},
{'x' : 136.53 * mm, 'y' : 0.00 * mm}, {'x' : 131.88 * mm, 'y' : -35.34 * mm},
{'x' : 118.24 * mm, 'y' : -68.27 * mm}, {'x' : 96.54 * mm, 'y' : -96.54 * mm},
{'x' : 68.27 * mm, 'y' : -118.24 * mm}, {'x' : 35.34 * mm, 'y' : -131.88 * mm},
{'x' : 0.00 * mm, 'y' : -136.53 * mm}, {'x' : -35.34 * mm, 'y' : -131.88 * mm},
{'x' : -68.27 * mm, 'y' : -118.24 * mm}, {'x' : -96.54 * mm, 'y' : -96.54 * mm},
{'x' : -118.24 * mm, 'y' : -68.27 * mm}, {'x' : -131.88 * mm, 'y' : -35.34 * mm},
{'x' : -106.20 * mm, 'y' : 0.00 * mm}, {'x' : -101.00 * mm, 'y' : 32.82 * mm},
{'x' : -85.92 * mm, 'y' : 62.42 * mm}, {'x' : -62.42 * mm, 'y' : 85.92 * mm},
{'x' : -32.82 * mm, 'y' : 101.00 * mm}, {'x' : 0.00 * mm, 'y' : 106.20 * mm},
{'x' : 32.82 * mm, 'y' : 101.00 * mm}, {'x' : 62.42 * mm, 'y' : 85.92 * mm},
{'x' : 85.92 * mm, 'y' : 62.42 * mm}, {'x' : 101.00 * mm, 'y' : 32.82 * mm},
{'x' : 106.20 * mm, 'y' : 0.00 * mm}, {'x' : 101.00 * mm, 'y' : -32.82 * mm},
{'x' : 85.92 * mm, 'y' : -62.42 * mm}, {'x' : 62.42 * mm, 'y' : -85.92 * mm},
{'x' : 32.82 * mm, 'y' : -101.00 * mm}, {'x' : 0.00 * mm, 'y' : -106.20 * mm},
{'x' : -32.82 * mm, 'y' : -101.00 * mm}, {'x' : -62.42 * mm, 'y' : -85.92 * mm},
{'x' : -85.92 * mm, 'y' : -62.42 * mm}, {'x' : -101.00 * mm, 'y' : -32.82 * mm},
{'x' : -75.87 * mm, 'y' : 0.00 * mm}, {'x' : -68.76 * mm, 'y' : 32.06 * mm},
{'x' : -47.75 * mm, 'y' : 58.96 * mm}, {'x' : -17.07 * mm, 'y' : 73.93 * mm},
{'x' : 15.77 * mm, 'y' : 74.21 * mm}, {'x' : 46.71 * mm, 'y' : 59.79 * mm},
{'x' : 68.19 * mm, 'y' : 33.26 * mm}, {'x' : 75.87 * mm, 'y' : 0.00 * mm},
{'x' : 68.76 * mm, 'y' : -32.06 * mm}, {'x' : 47.75 * mm, 'y' : -58.96 * mm},
{'x' : 17.07 * mm, 'y' : -73.93 * mm}, {'x' : -15.77 * mm, 'y' : -74.21 * mm},
{'x' : -46.71 * mm, 'y' : -59.79 * mm}, {'x' : -68.19 * mm, 'y' : -33.26 * mm},
{'x' : -45.00 * mm, 'y' : 0.00 * mm}, {'x' : -30.00 * mm, 'y' : 30.00 * mm},
{'x' : 0.00 * mm, 'y' : 45.00 * mm}, {'x' : 30.00 * mm, 'y' : 30.00 * mm},
{'x' : 45.00 * mm, 'y' : 0.00 * mm}, {'x' : 30.00 * mm, 'y' : -30.00 * mm},
{'x' : 0.00 * mm, 'y' : -45.00 * mm}, {'x' : -30.00 * mm, 'y' : -30.00 * mm},
{'x' : -15.00 * mm, 'y' : 0.00 * mm}, {'x' : 15.00 * mm, 'y' : 0.00 * mm},
{'x' : 41.15 * mm, 'y' : 123.44 * mm}, {'x' : 13.71 * mm, 'y' : 123.44 * mm},
{'x' : -13.71 * mm, 'y' : 123.44 * mm}, {'x' : -41.15 * mm, 'y' : 123.44 * mm},
{'x' : 82.29 * mm, 'y' : 96.00 * mm}, {'x' : 54.86 * mm, 'y' : 96.00 * mm},
{'x' : 27.43 * mm, 'y' : 96.00 * mm}, {'x' : 0.00 * mm, 'y' : 96.00 * mm},
{'x' : -27.43 * mm, 'y' : 96.00 * mm}, {'x' : -54.86 * mm, 'y' : 96.00 * mm},
{'x' : -82.29 * mm, 'y' : 96.00 * mm}, {'x' : 109.72 * mm, 'y' : 68.58 * mm},
{'x' : 82.29 * mm, 'y' : 68.58 * mm}, {'x' : 54.86 * mm, 'y' : 68.58 * mm},
{'x' : 27.43 * mm, 'y' : 68.58 * mm}, {'x' : 0.00 * mm, 'y' : 68.58 * mm},
{'x' : -27.43 * mm, 'y' : 68.58 * mm}, {'x' : -54.86 * mm, 'y' : 68.58 * mm},
{'x' : -82.29 * mm, 'y' : 68.58 * mm}, {'x' : -109.72 * mm, 'y' : 68.58 * mm},
{'x' : 123.44 * mm, 'y' : 41.15 * mm}, {'x' : 96.00 * mm, 'y' : 41.15 * mm},
{'x' : 68.58 * mm, 'y' : 41.15 * mm}, {'x' : 41.15 * mm, 'y' : 41.15 * mm},
{'x' : 13.71 * mm, 'y' : 41.15 * mm}, {'x' : -13.71 * mm, 'y' : 41.15 * mm},
{'x' : -41.15 * mm, 'y' : 41.15 * mm}, {'x' : -68.58 * mm, 'y' : 41.15 * mm},
{'x' : -96.00 * mm, 'y' : 41.15 * mm}, {'x' : -123.44 * mm, 'y' : 41.15 * mm},
{'x' : 123.44 * mm, 'y' : 13.71 * mm}, {'x' : 96.00 * mm, 'y' : 13.71 * mm},
{'x' : 68.58 * mm, 'y' : 13.71 * mm}, {'x' : 41.15 * mm, 'y' : 13.71 * mm},
{'x' : 13.71 * mm, 'y' : 13.71 * mm}, {'x' : -13.71 * mm, 'y' : 13.71 * mm},
{'x' : -41.15 * mm, 'y' : 13.71 * mm}, {'x' : -68.58 * mm, 'y' : 13.71 * mm},
{'x' : -96.00 * mm, 'y' : 13.71 * mm}, {'x' : -123.44 * mm, 'y' : 13.71 * mm},
{'x' : 123.44 * mm, 'y' : -13.71 * mm}, {'x' : 96.00 * mm, 'y' : -13.71 * mm},
{'x' : 68.58 * mm, 'y' : -13.71 * mm}, {'x' : 41.15 * mm, 'y' : -13.71 * mm},
{'x' : 13.71 * mm, 'y' : -13.71 * mm}, {'x' : -13.71 * mm, 'y' : -13.71 * mm},
{'x' : -41.15 * mm, 'y' : -13.71 * mm}, {'x' : -68.58 * mm, 'y' : -13.71 * mm},
{'x' : -96.00 * mm, 'y' : -13.71 * mm}, {'x' : -123.44 * mm, 'y' : -13.71 * mm},
{'x' : 123.44 * mm, 'y' : -41.15 * mm}, {'x' : 96.00 * mm, 'y' : -41.15 * mm},
{'x' : 68.58 * mm, 'y' : -41.15 * mm}, {'x' : 41.15 * mm, 'y' : -41.15 * mm},
{'x' : 13.71 * mm, 'y' : -41.15 * mm}, {'x' : -13.71 * mm, 'y' : -41.15 * mm},
{'x' : -41.15 * mm, 'y' : -41.15 * mm}, {'x' : -68.58 * mm, 'y' : -41.15 * mm},
{'x' : -96.00 * mm, 'y' : -41.15 * mm}, {'x' : -123.44 * mm, 'y' : -41.15 * mm},
{'x' : 109.72 * mm, 'y' : -68.58 * mm}, {'x' : 82.29 * mm, 'y' : -68.58 * mm},
{'x' : 54.86 * mm, 'y' : -68.58 * mm}, {'x' : 27.43 * mm, 'y' : -68.58 * mm},
{'x' : 0.00 * mm, 'y' : -68.58 * mm}, {'x' : -27.43 * mm, 'y' : -68.58 * mm},
{'x' : -54.86 * mm, 'y' : -68.58 * mm}, {'x' : -82.29 * mm, 'y' : -68.58 * mm},
{'x' : -109.72 * mm, 'y' : -68.58 * mm}, {'x' : 82.29 * mm, 'y' : -96.00 * mm},
{'x' : 54.86 * mm, 'y' : -96.00 * mm}, {'x' : 27.43 * mm, 'y' : -96.00 * mm},
{'x' : 0.00 * mm, 'y' : -96.00 * mm}, {'x' : -27.43 * mm, 'y' : -96.00 * mm},
{'x' : -54.86 * mm, 'y' : -96.00 * mm}, {'x' : -82.29 * mm, 'y' : -96.00 * mm},
{'x' : 41.15 * mm, 'y' : -123.44 * mm}, {'x' : 13.71 * mm, 'y' : -123.44 * mm},
{'x' : -13.71 * mm, 'y' : -123.44 * mm}, {'x' : -41.15 * mm, 'y' : -123.44 * mm},
{'x' : -197.15 * mm, 'y' : 0.00 * mm}, {'x' : -193.53 * mm, 'y' : 37.62 * mm},
{'x' : -182.79 * mm, 'y' : 73.85 * mm}, {'x' : -165.34 * mm, 'y' : 107.38 * mm},
{'x' : -139.41 * mm, 'y' : 139.41 * mm}, {'x' : -110.25 * mm, 'y' : 163.44 * mm},
{'x' : -77.03 * mm, 'y' : 181.48 * mm}, {'x' : -40.99 * mm, 'y' : 192.84 * mm},
{'x' : 0.00 * mm, 'y' : 197.15 * mm}, {'x' : 37.62 * mm, 'y' : 193.53 * mm},
{'x' : 73.85 * mm, 'y' : 182.79 * mm}, {'x' : 107.38 * mm, 'y' : 165.34 * mm},
{'x' : 139.41 * mm, 'y' : 139.41 * mm}, {'x' : 163.44 * mm, 'y' : 110.25 * mm},
{'x' : 181.48 * mm, 'y' : 77.03 * mm}, {'x' : 192.84 * mm, 'y' : 40.99 * mm},
{'x' : 197.15 * mm, 'y' : 0.00 * mm}, {'x' : 193.53 * mm, 'y' : -37.62 * mm},
{'x' : 182.79 * mm, 'y' : -73.85 * mm}, {'x' : 165.34 * mm, 'y' : -107.38 * mm},
{'x' : 139.41 * mm, 'y' : -139.41 * mm}, {'x' : 110.25 * mm, 'y' : -163.44 * mm},
{'x' : 77.03 * mm, 'y' : -181.48 * mm}, {'x' : 40.99 * mm, 'y' : -192.84 * mm},
{'x' : 0.00 * mm, 'y' : -197.15 * mm}, {'x' : -37.62 * mm, 'y' : -193.53 * mm},
{'x' : -73.85 * mm, 'y' : -182.79 * mm}, {'x' : -107.38 * mm, 'y' : -165.34 * mm},
{'x' : -139.41 * mm, 'y' : -139.41 * mm}, {'x' : -163.44 * mm, 'y' : -110.25 * mm},
{'x' : -181.48 * mm, 'y' : -77.03 * mm}, {'x' : -192.84 * mm, 'y' : -40.99 * mm},
{'x' : 197.15 * mm, 'y' : 0.00 * mm}, {'x' : 193.53 * mm, 'y' : 37.62 * mm},
{'x' : 182.79 * mm, 'y' : 73.85 * mm}, {'x' : 165.34 * mm, 'y' : 107.38 * mm},
{'x' : 139.41 * mm, 'y' : 139.41 * mm}, {'x' : 110.25 * mm, 'y' : 163.44 * mm},
{'x' : 77.03 * mm, 'y' : 181.48 * mm}, {'x' : 40.99 * mm, 'y' : 192.84 * mm},
{'x' : 0.00 * mm, 'y' : 197.15 * mm}, {'x' : -37.62 * mm, 'y' : 193.53 * mm},
{'x' : -73.85 * mm, 'y' : 182.79 * mm}, {'x' : -107.38 * mm, 'y' : 165.34 * mm},
{'x' : -139.41 * mm, 'y' : 139.41 * mm}, {'x' : -163.44 * mm, 'y' : 110.25 * mm},
{'x' : -181.48 * mm, 'y' : 77.03 * mm}, {'x' : -192.84 * mm, 'y' : 40.99 * mm},
{'x' : -197.15 * mm, 'y' : 0.00 * mm}, {'x' : -193.53 * mm, 'y' : -37.62 * mm},
{'x' : -182.79 * mm, 'y' : -73.85 * mm}, {'x' : -165.34 * mm, 'y' : -107.38 * mm},
{'x' : -139.41 * mm, 'y' : -139.41 * mm}, {'x' : -110.25 * mm, 'y' : -163.44 * mm},
{'x' : -77.03 * mm, 'y' : -181.48 * mm}, {'x' : -40.99 * mm, 'y' : -192.84 * mm},
{'x' : 0.00 * mm, 'y' : -197.15 * mm}, {'x' : 37.62 * mm, 'y' : -193.53 * mm},
{'x' : 73.85 * mm, 'y' : -182.79 * mm}, {'x' : 107.38 * mm, 'y' : -165.34 * mm},
{'x' : 139.41 * mm, 'y' : -139.41 * mm}, {'x' : 163.44 * mm, 'y' : -110.25 * mm},
{'x' : 181.48 * mm, 'y' : -77.03 * mm}, {'x' : 192.84 * mm, 'y' : -40.99 * mm},
]
# PMT gains
# Extracted from Zurich's Xenon100 PMT gain database using examples/extract_gain
# File used: all120326_1544.gain
# A few of these gains are zero: we'll assume these PMTs are turned off.
# PMT 0 does not exist (real Xenon100 PMTs start from 0), so it gets gain 0.
gains = [
# 0 -- PMT zero is fake!
0,
# 1 # 2 # 3 # 4 # 5
2675000.0, 2958000.0, 1936000.0, 2326000.0, 1964000.0,
# 6 # 7 # 8 # 9 # 10
1971000.0, 2104000.0, 1999000.0, 0.0, 2102000.0,
# 11 # 12 # 13 # 14 # 15
2044000.0, 0.0, 2177000.0, 2180000.0, 2265000.0,
# 16 # 17 # 18 # 19 # 20
2293000.0, 2177000.0, 2331000.0, 2099000.0, 2096000.0,
# 21 # 22 # 23 # 24 # 25
1899000.0, 2111000.0, 1874000.0, 1948000.0, 2106000.0,
# 26 # 27 # 28 # 29 # 30
2121000.0, 1987000.0, 1889000.0, 2473000.0, 2161000.0,
# 31 # 32 # 33 # 34 # 35
2192000.0, 2329000.0, 1112000.0, 2157000.0, 2106000.0,
# 36 # 37 # 38 # 39 # 40
2182000.0, 2001000.0, 1921000.0, 0.0, 2121000.0,
# 41 # 42 # 43 # 44 # 45
1852000.0, 1878000.0, 2088000.0, 1974000.0, 1940000.0,
# 46 # 47 # 48 # 49 # 50
2134000.0, 2132000.0, 2018000.0, 2207000.0, 2237000.0,
# 51 # 52 # 53 # 54 # 55
2201000.0, 1985000.0, 2173000.0, 2126000.0, 2288000.0,
# 56 # 57 # 58 # 59 # 60
2140000.0, 2170000.0, 0.0, 2408000.0, 2253000.0,
# 61 # 62 # 63 # 64 # 65
2109000.0, 2134000.0, 1979000.0, 2267000.0, 2149000.0,
# 66 # 67 # 68 # 69 # 70
2164000.0, 2077000.0, 2170000.0, 2223000.0, 2325000.0,
# 71 # 72 # 73 # 74 # 75
2122000.0, 2343000.0, 2312000.0, 2090000.0, 1944000.0,
# 76 # 77 # 78 # 79 # 80
2091000.0, 1948000.0, 1974000.0, 2098000.0, 2134000.0,
# 81 # 82 # 83 # 84 # 85
2184000.0, 1992000.0, 2150000.0, 1980000.0, 1878000.0,
# 86 # 87 # 88 # 89 # 90
2093000.0, 2162000.0, 1901000.0, 2120000.0, 2059000.0,
# 91 # 92 # 93 # 94 # 95
2281000.0, 2214000.0, 2143000.0, 1943000.0, 1934000.0,
# 96 # 97 # 98 # 99 # 100
2410000.0, 2227000.0, 1843000.0, 1881000.0, 0.0,
# 101 # 102 # 103 # 104 # 105
1965000.0, 2368000.0, 1938000.0, 1981000.0, 0.0,
# 106 # 107 # 108 # 109 # 110
1732000.0, 2091000.0, 1932000.0, 2080000.0, 2145000.0,
# 111 # 112 # 113 # 114 # 115
1932000.0, 1806000.0, 1939000.0, 1765000.0, 2111000.0,
# 116 # 117 # 118 # 119 # 120
2001000.0, 1917000.0, 2082000.0, 2043000.0, 2027000.0,
# 121 # 122 # 123 # 124 # 125
1833000.0, 1972000.0, 2030000.0, 2139000.0, 1946000.0,
# 126 # 127 # 128 # 129 # 130
1988000.0, 1967000.0, 2190000.0, 2217000.0, 2092000.0,
# 131 # 132 # 133 # 134 # 135
2252000.0, 2170000.0, 2014000.0, 1953000.0, 1997000.0,
# 136 # 137 # 138 # 139 # 140
1966000.0, 1854000.0, 2098000.0, 1639000.0, 2229000.0,
# 141 # 142 # 143 # 144 # 145
1759000.0, 1987000.0, 1911000.0, 1858000.0, 1653000.0,
# 146 # 147 # 148 # 149 # 150
2036000.0, 1716000.0, 0.0, 1907000.0, 2165000.0,
# 151 # 152 # 153 # 154 # 155
1833000.0, 2126000.0, 2119000.0, 1763000.0, 1990000.0,
# 156 # 157 # 158 # 159 # 160
1869000.0, 1874000.0, 2030000.0, 2152000.0, 1917000.0,
# 161 # 162 # 163 # 164 # 165
1749000.0, 1668000.0, 2085000.0, 1886000.0, 1692000.0,
# 166 # 167 # 168 # 169 # 170
2094000.0, 1446000.0, 1773000.0, 2023000.0, 1737000.0,
# 171 # 172 # 173 # 174 # 175
1859000.0, 1887000.0, 1872000.0, 2013000.0, 2078000.0,
# 176 # 177 # 178 # 179 # 180
1796000.0, 0.0, 1770000.0, 2018000.0, 2220000.0,
# 181 # 182 # 183 # 184 # 185
2544000.0, 978900.0, 1795000.0, 1445000.0, 1988000.0,
# 186 # 187 # 188 # 189 # 190
2032000.0, 1915000.0, 2143000.0, 2096000.0, 0.0,
# 191 # 192 # 193 # 194 # 195
0.0, 2081000.0, 1730000.0, 1637000.0, 0.0,
# 196 # 197 # 198 # 199 # 200
1936000.0, 1665000.0, 1958000.0, 1976000.0, 1975000.0,
# 201 # 202 # 203 # 204 # 205
1885000.0, 2101000.0, 2014000.0, 1997000.0, 2001000.0,
# 206 # 207 # 208 # 209 # 210
1993000.0, 1915000.0, 2113000.0, 1985000.0, 1813000.0,
# 211 # 212 # 213 # 214 # 215
2156000.0, 2041000.0, 2060000.0, 1890000.0, 2162000.0,
# 216 # 217 # 218 # 219 # 220
1810000.0, 1988000.0, 1983000.0, 1946000.0, 1941000.0,
# 221 # 222 # 223 # 224 # 225
2134000.0, 1829000.0, 1996000.0, 0.0, 1903000.0,
# 226 # 227 # 228 # 229 # 230
2096000.0, 2150000.0, 1990000.0, 1949000.0, 1870000.0,
# 231 # 232 # 233 # 234 # 235
2070000.0, 1946000.0, 1902000.0, 2128000.0, 0.0,
# 236 # 237 # 238 # 239 # 240
1946000.0, 1652000.0, 1986000.0, 1852000.0, 1909000.0,
# 241 # 242
1932000.0, 2013000.0,
]
# Sigmas of the 1pe peak in the gain spectrum, from same file
gain_sigmas = [
# 0 -- PMT zero is fake!
0,
# 1 # 2 # 3 # 4 # 5
931800.0, 891900.0, 786300.0, 1035000.0, 947800.0,
# 6 # 7 # 8 # 9 # 10
932700.0, 1167000.0, 976400.0, 0.0, 1154000.0,
# 11 # 12 # 13 # 14 # 15
1168000.0, 0.0, 956500.0, 1235000.0, 1250000.0,
# 16 # 17 # 18 # 19 # 20
1611000.0, 1203000.0, 1084000.0, 1333000.0, 1006000.0,
# 21 # 22 # 23 # 24 # 25
1051000.0, 935600.0, 1014000.0, 1281000.0, 1147000.0,
# 26 # 27 # 28 # 29 # 30
608800.0, 941200.0, 959700.0, 1464000.0, 1180000.0,
# 31 # 32 # 33 # 34 # 35
1186000.0, 1244000.0, 542500.0, 1146000.0, 951200.0,
# 36 # 37 # 38 # 39 # 40
1274000.0, 1075000.0, 1181000.0, 0.0, 1153000.0,
# 41 # 42 # 43 # 44 # 45
895700.0, 921500.0, 1106000.0, 997000.0, 1030000.0,
# 46 # 47 # 48 # 49 # 50
1116000.0, 1167000.0, 1112000.0, 1172000.0, 1327000.0,
# 51 # 52 # 53 # 54 # 55
1134000.0, 978500.0, 1070000.0, 1114000.0, 1229000.0,
# 56 # 57 # 58 # 59 # 60
1102000.0, 1065000.0, 0.0, 1193000.0, 1194000.0,
# 61 # 62 # 63 # 64 # 65
1142000.0, 1117000.0, 1079000.0, 1333000.0, 1079000.0,
# 66 # 67 # 68 # 69 # 70
1165000.0, 1080000.0, 1107000.0, 1293000.0, 1248000.0,
# 71 # 72 # 73 # 74 # 75
1303000.0, 1227000.0, 1224000.0, 1048000.0, 930300.0,
# 76 # 77 # 78 # 79 # 80
1351000.0, 1056000.0, 997500.0, 1203000.0, 1210000.0,
# 81 # 82 # 83 # 84 # 85
1033000.0, 1050000.0, 1300000.0, 1042000.0, 1110000.0,
# 86 # 87 # 88 # 89 # 90
1106000.0, 1326000.0, 1185000.0, 1261000.0, 1136000.0,
# 91 # 92 # 93 # 94 # 95
1212000.0, 1210000.0, 1281000.0, 1270000.0, 1141000.0,
# 96 # 97 # 98 # 99 # 100
1374000.0, 1220000.0, 993000.0, 1221000.0, 0.0,
# 101 # 102 # 103 # 104 # 105
1010000.0, 1248000.0, 924900.0, 975400.0, 0.0,
# 106 # 107 # 108 # 109 # 110
908800.0, 1026000.0, 1039000.0, 985600.0, 1290000.0,
# 111 # 112 # 113 # 114 # 115
1271000.0, 1054000.0, 941500.0, 988600.0, 1087000.0,
# 116 # 117 # 118 # 119 # 120
1030000.0, 880400.0, 870500.0, 991400.0, 899400.0,
# 121 # 122 # 123 # 124 # 125
878000.0, 991800.0, 1090000.0, 1113000.0, 936000.0,
# 126 # 127 # 128 # 129 # 130
957700.0, 940200.0, 1219000.0, 1326000.0, 1234000.0,
# 131 # 132 # 133 # 134 # 135
1212000.0, 1184000.0, 974400.0, 942600.0, 929500.0,
# 136 # 137 # 138 # 139 # 140
1023000.0, 1037000.0, 950000.0, 1064000.0, 1151000.0,
# 141 # 142 # 143 # 144 # 145
881900.0, 960500.0, 879600.0, 907300.0, 948400.0,
# 146 # 147 # 148 # 149 # 150
834400.0, 966800.0, 0.0, 926600.0, 906700.0,
# 151 # 152 # 153 # 154 # 155
872100.0, 859100.0, 976100.0, 747200.0, 839000.0,
# 156 # 157 # 158 # 159 # 160
943700.0, 1224000.0, 1071000.0, 1123000.0, 814200.0,
# 161 # 162 # 163 # 164 # 165
888900.0, 1000000.0, 991900.0, 873800.0, 917900.0,
# 166 # 167 # 168 # 169 # 170
875000.0, 736700.0, 1083000.0, 822400.0, 1020000.0,
# 171 # 172 # 173 # 174 # 175
1088000.0, 1069000.0, 863000.0, 925700.0, 939000.0,
# 176 # 177 # 178 # 179 # 180
1114000.0, 0.0, 927100.0, 851500.0, 920800.0,
# 181 # 182 # 183 # 184 # 185
1285000.0, 874700.0, 1045000.0, 746600.0, 1415000.0,
# 186 # 187 # 188 # 189 # 190
895900.0, 1191000.0, 982100.0, 900900.0, 0.0,
# 191 # 192 # 193 # 194 # 195
0.0, 1268000.0, 1362000.0, 1308000.0, 0.0,
# 196 # 197 # 198 # 199 # 200
1287000.0, 1156000.0, 1169000.0, 1411000.0, 1127000.0,
# 201 # 202 # 203 # 204 # 205
876800.0, 1350000.0, 1131000.0, 1249000.0, 1477000.0,
# 206 # 207 # 208 # 209 # 210
1218000.0, 1122000.0, 1093000.0, 1275000.0, 894900.0,
# 211 # 212 # 213 # 214 # 215
928500.0, 1045000.0, 1009000.0, 894700.0, 885000.0,
# 216 # 217 # 218 # 219 # 220
947500.0, 991300.0, 1087000.0, 1160000.0, 1138000.0,
# 221 # 222 # 223 # 224 # 225
1124000.0, 1156000.0, 1090000.0, 0.0, 944000.0,
# 226 # 227 # 228 # 229 # 230
1113000.0, 1335000.0, 1182000.0, 983200.0, 1153000.0,
# 231 # 232 # 233 # 234 # 235
1335000.0, 1344000.0, 1103000.0, 1288000.0, 0.0,
# 236 # 237 # 238 # 239 # 240
1173000.0, 1226000.0, 942800.0, 1117000.0, 1182000.0,
# 241 # 242
1042000.0, 1096000.0,
]
[NeuralNet.PosRecNeuralNet]
# Number of neurons in the hidden layer
# Used to check if the number of weights and biases are correct
hidden_layer_neurons = 30
# Neural network outputs position in this unit. Will be converted to pax units.
nn_output_unit = mm
# Biases used by the neuron activation functions, taken from nn-missing_9_12_39_58.c
# Input neuron's don't use a bias, so there should be hidden_layer_neurons + 2 biases
# In nn-missing_9_12_39_58.c input neurons had biases, but they were unusued (random [-1,1]?)
biases = [ 0.89283, 0.89691, -0.93983, -1.07323,
-0.8617 , -1.32323, 0.88486, 1.25378, 0.12575, -0.83402,
-0.76243, 1.08109, -0.75161, 1.03818, 0.26586, 0.37391,
0.31579, -1.16333, -1.06323, -0.80685, 1.24958, -0.00742,
1.14161, 1.24745, -0.10054, 0.4158 , 1.07301, 0.6385 ,
0.23595, 1.05783, 0.96483, -1.72249]
# Weights of the connections, taken from nn-missing_9_12_39_58.c
# The first n_top_pmts * hidden_layer_neurons are for the connections from the input to the hidden layer:
# The first 98 for the first hidden layer neuron, the next 98 of the second hidden layer neuron, etc
# The next hidden_layer_neurons * 2 are for the connections from the hidden to the output layer
# The first hidden_layer_neurons for the x-output neuron, the next hidden_layer_neurons for the y-output neuron
weights = 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