Analysis A. Aguasca-Cabot lstchain v0.7.X

Overview

• Source-dependent analysis using LSTCHAIN v0.7.5, cleaning method tail cut 84 and dynamic cleaning.
• Observations used in the analysis: 5580-5584, 5630-5639 and 5696-5704.
• Results presented in the LST General meeting (Nov. 2021), link here.

Monte Carlo information

• Link to MC files used:
  • Particle types: gamma, proton
  • ZD (deg): 40
  • AZ (deg): 180

DL1 data

Real data

• Original DL1a files

/fefs/aswg/data/real/DL1/{20210809,20210810,20210812}/v0.7.3/tailcut84/dl1_LST-1.Run0XXXX.XXXX.h5

Added dynamic cleaning using srcipt `lstchain_dl1ab.py` by LSTCHAIN v0.7.5.

 "dynamic_cleaning": {
   "apply": true,
   "threshold": 267,
   "fraction_cleaning_intensity": 0.03
 },

• Produced DL1b files

MC data

• Original DL1a files

/fefs/aswg/data/mc/DL1/20200629_prod5_trans_80/{particle}/{zenith}/{azimuth}/20210506_v0.7.3_prod5_trans_80_zen40deg_local_tailcut_8_4

Added source dependent analysis using script `lstchain_add_source_dependent_parameters.py` and dynamic cleaning using srcipt `lstchain_dl1ab.py` by LSTCHAIN v0.7.5.

 "dynamic_cleaning": {
   "apply": true,
   "threshold": 267,
   "fraction_cleaning_intensity": 0.03
 },

• Produced DL1b files

Random forest

• LSTCHAIN v0.7.5
• source-dependent, dispnorm

/fefs/aswg/workspace/arnau.aguasca/scripts/_configs/XXXXXXXXX

DL3 data

Performed the Dl2 to dl3 stage and IRF production with Seiya's fork of LSTCHAIN. Repository used: https://github.com/aaguasca/cta-lstchain

• IRF: point-like, single-offset

/fefs/aswg/workspace/arnau.aguasca/scripts/_configs/RSOph/LSTGeneralMeeting/lstchain_dl3_config.json

• Quality cuts

 "EventSelector": {
   "filters": {
     "intensity": [100, Infinity],
     "width": [0, Infinity],
     "length": [0, Infinity],
     "r": [0, 1],
     "wl": [0.1, 1],
     "leakage_intensity_width_2": [0, 0.2]
   }
 },
 "DL3FixedCuts": {
   "fixed_gh_cut": 0.6,
   "fixed_alpha_cut": 20,
   "allowed_tels": [1]
 }