example_em-trk_job.fcl

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#include "services_dune.fcl"
#include "imagepatternalgs.fcl"

# Example configuration of job applying CNN-based selection of
# EM-like activity (3 outputs: [track, em, none]) on the level
# of tracks / clusters / single hits.
# The same configuration can be used for other CNN models, trained
# to distinguish also Michel electrons or tagging vertices.
# Configured for ProtoDUNE, hopefully straight-forward to adopt
# to other experiments.

process_name: EmTrackId

services:
{
  TFileService: { fileName: "reco_hist.root" }
  MemoryTracker:     {}
  TimeTracker:       {}
  RandomNumberGenerator: {} #ART native random number generator
  message:              @local::dune_message_services_prod_debug
  FileCatalogMetadata:  @local::art_file_catalog_mc
                        @table::protodune_services
}

source:
{
  module_type: RootInput
  maxEvents:  -1        # Number of events to create
}

physics:
{
 producers:
 {
  #emtrackid:      @local::standard_emtrackclusterid # uses 3-outs CNN model
  emtrkmichelid:  @local::standard_emtrackmichelid   # uses 4-outs model
 }

 reco: [ emtrkmichelid ]
 stream1: [ out1 ]

 trigger_paths: [ reco ]
 end_paths:     [ stream1 ]
}

outputs:
{
 out1:
 {
   module_type: RootOutput
   fileName:    "%ifb_%tc_reco.root"
   dataTier:    "full-reconstructed"
   compressionLevel: 1
 }
}

# Model configuration:
#
physics.producers.emtrkmichelid.PointIdAlg.NNetModelFile:      "/home/your_dir/your_cnn_model.nnet" # your model (there is no default one in repository!)
physics.producers.emtrkmichelid.PointIdAlg.NNetOutputs:        ["track", "em", "michel", "none"]    # be sure that the order corresponds to your model outputs
physics.producers.emtrkmichelid.PointIdAlg.PatchSizeW:         44     # how many wires in patch
physics.producers.emtrkmichelid.PointIdAlg.PatchSizeD:         48     # how many downsampled ADC entries
physics.producers.emtrkmichelid.PointIdAlg.DriftWindow:        6      # downsampling window in drift ticks
physics.producers.emtrkmichelid.PointIdAlg.DownscaleFn:        "mean" # downsampling function
physics.producers.emtrkmichelid.PointIdAlg.DownscaleFullView:  false  # downsample after the patch position is selected

# Input selection:
#
physics.producers.emtrkmichelid.HitModuleLabel:                "linecluster" # all hits (used to classify leftovers from clustering)
physics.producers.emtrkmichelid.ClusterModuleLabel:            "linecluster" # clusters to be classified
physics.producers.emtrkmichelid.TrackModuleLabel:              "pmtrack"     # tracks to be classified (leave that one empty if there are no 3D tracks yet)
physics.producers.emtrkmichelid.Views:                         []  # possible to run over selected views only (all views if empty list)

# Calibration,  NOTE: use the same settings as for the training data preparation
#
#physics.producers.emtrkmichelid.PointIdAlg.CalorimetryAlg:  @local::standard_calorimetryalgmc # use your detector calibration constants, this the default
#physics.producers.emtrkmichelid.PointIdAlg.CalibrateAmpl:   true                              # this equalizes ADC amplitudes between planes and MC/data

# Noise and distortions testing (off by default):
#
#physics.producers.emtrkmichelid.PointIdAlg.BlurKernel:      [0.03, 0.15, 0.64, 0.15, 0.03] # wire to wire blur
#physics.producers.emtrkmichelid.PointIdAlg.NoiseSigma:      4                              # uncorrelated noise
#physics.producers.emtrkmichelid.PointIdAlg.CoherentSigma:   6                              # coherent noise, over 32 wire groups