Directionality pipeline to analyze data from INAF

• Reconstruct data

  • Data with Sr
  • data with Fe/Cd

• Run directionality Code on Sr only https://github.com/fiorotto8/CygnoAnal

  • Usually output a 'AfterDir'
  • It is better to run on Condor and use splitROOT.py to split the input file (usually 'reco')

• Run calibration.py on the Fe and Cd reco dataset

  • Usually output contain 'calib' in the name

• Run DistrfromDirectionality.py on directionality data with the calibration parameters

  • Usually takes in input an 'AfterDir' file and output a 'anal' file
  • Typical Impact point selection: (df['X_ImpactPoint'] > 1700) & (df['X_ImpactPoint'] < 1800) & (df['Y_ImpactPoint'] > 850) & (df['Y_ImpactPoint'] < 1400)
  • Typical containment selection: (df['Ymin'] >500) & (df['Ymax'] <2304-500) & (df["Xmin"]>500)

• Run deconvolveDistr.py with Geant4 anlayzed data and the measured directionalities to get the deconvoluted intrinsic angular resolution

  • Needs Geant4 90Sr simulation https://github.com/fiorotto8/MANGO_RadioactiveSource
  • Usually contains 'deconvolved'

• Run modulation_factor.py with deconvolved data and efficiency data

  • Needs Geant4 gamma efficiency simulation https://github.com/fiorotto8/MANGOspaceG4
  • Usually contains 'modulation'
  • Usually contains efficiency

• the script createPlot.sh is running the plot_multiGraph.py multiple times to create 'nice' canvas of some interesting quantities cycling over all the measurements