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.
• 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.

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,

• 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_NOpsf_srcdep/dec_min_413/

• Source-independent

- Processed with lstMCpipe using LSTCHAIN v0.9.6.

- Path:

/fefs/aswg/workspace/MC_data_simlink/models/AllSky/galsource_min_413_tuned_nsb_NOpsf/dec_min_413/

Notice that the path moved from aswg/data to aswg/workspace/MC_data_simlink. The MC prod in aswg/data has a smeared PSF (PSF tuning).

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. I did the following:

• Procedure

1- I weighted the Testing DL2 MC dataset to the spectral index of RS Oph without considering RS Oph amplitude, i.e. maximizing the surviving MC gammas :following a spectra index of -4.

2- Cut in theta (< 0.3 deg) and apply filter (remove the contribution of events due to the long tails of the PSF)

3- Obtain the gammaness cut for a percentile of 90% survival gammas for each energy bin: Energy array with 5 bins per decade

4- Apply the energy dependent cuts in gammaness to the unweighted testing MC DL2 files to obtain the IRFs

DL3 data selection

• - Quality cuts

 "EventSelector": {
   "filters": {
     "intensity": [50, Infinity],
     "width": [0, Infinity],
     "length": [0, Infinity],
     "r": [0, 1],
     "wl": [0.01, 1],
     "leakage_intensity_width_2": [0, 0.2],
     "event_type" : [32, 32]
   }
 },
 "DL3FixedCuts": {
   "fixed_gh_cut": 0.6,
   "gh_efficiency": 0.9,
   "fixed_theta_cut": 0.2,
   "theta_containment": 0.68,
   "alpha_containment": 0.68,
   "allowed_tels": [1]
 }

High-level analysis

• Source-dependent

  - IRF: point-like, single-offset

/fefs/aswg/workspace/arnau.aguasca/scripts/_configs/DL3IRF-generated-configs/config_int50_leak0.2_gh0.7_geff0.9_th0.2_tc0.68_a15_ac0.68.json

- Produced DL3 files

/fefs/aswg/workspace/arnau.aguasca/Analysis/results/real/DL3/RSOph/nearest_node/...

• 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.