Analysis A. Aguasca-Cabot lstchain v0.9.X AllSky

Overview

• Source-dependent/independent analysis using LSTCHAIN v0.9.6 and the branch "interp_irfs" with last commit e5835d5ab7792cbb528118de9cebe4a60f7802a8. The MC production processed with LSTOSA v0.8.2 using LSTCHAIN v0.9.6
• Cleaning method tail cut 84 and dynamic cleaning. Also, the MC files are tuned to match the NSB in the FoV and the PSF.
• Observations used in the analysis:

Monte Carlo information

• AllSky MC prodution.
• Link to MC files used: /fefs/aswg/data/mc/DL1/AllSky/galsource_min_413_tuned_nsb/...

- Particle types: gamma diffuse and protons

- MC prod (deg): galsource_min_413_tuned_nsb

- Dec band (deg): dec_min_431

- Other information: MC production through PR in lstMCpipe Github webpage (https://github.com/cta-observatory/lstmcpipe/tree/master/production_configs/20220523_dec_413_tuned_nsb)

- MC files are tuned to match the NSB in the FoV and the PSF.

DL1 data

• Processed with LSTOSA using LSTCHAIN v0.9.6.

- Dynamic cleaning and tail cut cleaning with pedestal threshold applied by the the standard parameters using pipeline LSTOSA.

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

Real data

• Parameters for the tail cut cleaning with pedestal threshold

 "tailcuts_clean_with_pedestal_threshold": {
   "picture_thresh":8,
   "boundary_thresh":4,
   "sigma":2.5,
   "keep_isolated_pixels":false,
   "min_number_picture_neighbors":2,
   "use_only_main_island":false,
   "delta_time": 2
 }

• Original DL1a files (processed up to DL1 by LSTOSA using LSTCHAIN v0.9.2)

/fefs/aswg/data/real/DL1/{}/v0.9/tailcut84/dl1_LST-1.RunXXXXX.XXXX.h5

MC data

• Tuned NSB

   "increase_nsb": true,
   "extra_noise_in_dim_pixels": 0.937,
   "extra_bias_in_dim_pixels": 0.323,
   "transition_charge": 8,
   "extra_noise_in_bright_pixels": 1.041,

• Tuned PSF

   "increase_psf": true,
   "smeared_light_fraction": 0.175

• Original DL1a files

/fefs/aswg/data/mc/DL1/AllSky/galsource_min_413_tuned_nsb/{TrainingDataset,TestingDataset}/dec_min_413/{particle}/node...

Random forest

• Dispnorm parameterisation

• Source-dependent

- LSTCHAIN v0.9.6

- Config file:

/fefs/aswg/workspace/arnau.aguasca/scripts/_configs/lstchain_src_dep_tailcut84_config_dispnorm_v0.9.4.json

- path:

/fefs/aswg/workspace/MC_data_simlink/models/AllSky/galsource_min_413_tuned_nsb_srcdep/dec_min_413/

• Source-independent

- Processed with lstMCpipe using LSTCHAIN v0.9.6.

- Path:

/fefs/aswg/data/models/AllSky/galsource_min_413_tuned_nsb/dec_min_413/

DL1 to DL2 data

Real data

• Source-dependent

- LSTCHAIN v0.9.6

- Config file:

/fefs/aswg/workspace/arnau.aguasca/scripts/_configs/RSOph/analysis_v0.9.6/lstchain_config_srcdep_lstmcpipe_tailcut84_dispnorm_v0.9.6.json

• Source-independent

- LSTCHAIN v0.9.6

- Config file:

/fefs/aswg/workspace/arnau.aguasca/scripts/_configs/RSOph/analysis_v0.9.6/lstchain_config_lstmcpipe_tailcut84_dispnorm_v0.9.6.json

MC data

• Source-dependent

- Processed with lstMCpipe using LSTCHAIN v0.9.6.

- Config file:

/fefs/aswg/workspace/arnau.aguasca/scripts/_configs/RSOph/analysis_v0.9.4/lstchain_config_srcdep_lstmcpipe_tailcut84_dispnorm_v0.9.6.json

• Source-independent

- Processed with lstMCpipe using LSTCHAIN v0.9.6.

- Config file:

/fefs/aswg/data/models/AllSky/galsource_min_413_tuned_nsb/dec_min_413/lstchain_config_2022-05-23.json

Quality cuts optimization

Since the AllSky MC is a tailored production than the Fixed MC production, we cannot use high-zenith Crab observations to find the best cuts. Thus, we have to use MC data to find the best cuts for RS Oph.

MC gammas:

Background data:

• Procedure

-Weight the distribution of MC gammas according to the spectral index of RS Oph ($\Gamma = -4$) and randomly select events according to this distribution. -Compute the efficiency cuts for each energy bin -For each energy bin, compute the total rate of gammas and background in each energy bin. -Calculate the number of gammas and background as the rate time the observation time we want for the sensitivity (50 hours) -Compute the sensitivity -Find the best cuts

DL3 data selection

• Source-dependent

  - Quality cuts

 "EventSelector": {
   "filters": {
     "intensity": [50, Infinity],
     "width": [0, Infinity],
     "length": [0, Infinity],
     "r": [0, 1],
     "wl": [0.1, 1],
     "leakage_intensity_width_2": [0, 0.2],
     "event_type" : [32, 32]
   }
 },
 "DL3FixedCuts": {
   "fixed_gh_cut": 0.9,
   "fixed_alpha_cut": 10,
   "allowed_tels": [1]
 }

• Source-independent

  - Quality cuts

 "EventSelector": {
   "filters": {
     "intensity": [50, Infinity],
     "width": [0, Infinity],
     "length": [0, Infinity],
     "r": [0, 1],
     "wl": [0.1, 1],
     "leakage_intensity_width_2": [0, 0.2],
     "event_type" : [32, 32]
   }
 },
 "DL3FixedCuts": {
   "fixed_gh_cut": 0.8,
   "fixed_theta_cut": 0.141,
   "allowed_tels": [1]
 }

High-level analysis

• Source-dependent

  - IRF: point-like, single-offset

/fefs/aswg/workspace/arnau.aguasca/scripts/_configs/RSOph/XXXX

- Produced DL3 files

/fefs/aswg/workspace/arnau.aguasca/XXXX

• Source-independent

Analysis results

• High level analysis performed with gammapy-v0.20.1

Alpha plot

• Filters in EventSelector applied:

 "EventSelector": {
   "filters": {
     "intensity": [50, Infinity],
     "width": [0, Infinity],
     "length": [0, Infinity],
     "r": [0, 1],
     "wl": [0.1, 1],
     "leakage_intensity_width_2": [0, 0.2]
   }
 }

See section Quality cuts optimization to know more about the justification of the applied cuts.