GRB221009A srcdep analysis

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General information

• Analysis by Kenta Terauchi (Kyoto University - terauchi.kenta.74s@st.kyoto-u.ac.jp)

Superplots

• 

  Superplots on 2022-10-10. Made by Seiya

NSB level

• 

  Median of pedestal std on 2022-10-10
• 

  Median of pedestal std on 2022-10-12

Calibrations

• 

  Pedestal std distribution of run9602(1st subrun)
• 

  Pedestal std distribution of run9602(1st subrun). Calibration was processed with new calibration file made by Franca

• 

  Pedestal std distribution in the camera frame (run9602;1st subrun).
• 

  Pedestal std distribution in the camera frame (run9602;1st subrun). Calibration was processed with new calibration file made by Franca

Image cleaning

• Cleaning method: tailcut cleaning w/ pedestal cleaning, dynamic cleaning
• Cleaning threshold: (picture threshold, boundary threshold)

The following threshold values are obtained so that ~5% of pedestal events survive after image cleaning. For now, the ratio of picture threshold to boundary threshold are fixed to 2:1 (same as default ratio, 8:4).

<2022_10_10>
run9602 - 9603: (24, 12)
run9604: (26, 13)
run9605 - 9607: (28, 14)

<2022_10_12>
run9613: (26, 13)
run9614: (18,9)
run9615 - 9616: (20, 10)
run9617: (22, 11)

• Sigma value for pedestal cleaning: 2.5 (default)

NSB tuning of MC data

I produced run-wise MC taking into account the NSB level, cleaning threshold, and Zd/Az range of each run.

Monte Carlo information

• Link to MC files used (base): /fefs/aswg/data/mc/DL1/AllSky/20221027_v0.9.9_base_prod/*
• Particle types: standard particle types for AllSky MC prodution
• DEC band: dec_2276

MC productions

• lstchain v0.9.10
• lstMCpipe v0.10
• NSB tuning method: Adding NSB noise to DL1 images. (Adding NSB noise to R0 waveform is VERY time consuming! It makes not so much sense to tune waveform for now. Maybe we could produce more dedicated MC in later stage before publication)

Date: 2022-10-10

• run9602

"image_modifier": {
   "increase_nsb": true,
   "extra_noise_in_dim_pixels": 28.061,
   "extra_bias_in_dim_pixels": 6.6885,
   "transition_charge": 8,
   "extra_noise_in_bright_pixels": 69.279,
   "increase_psf": false,
   "smeared_light_fraction": 0
 },

• run9603

"image_modifier": {
   "increase_nsb": true,
   "extra_noise_in_dim_pixels": 29.361,
   "extra_bias_in_dim_pixels": 6.632,
   "transition_charge": 8,
   "extra_noise_in_bright_pixels": 78.425,
   "increase_psf": false,
   "smeared_light_fraction": 0
 }

• run9604

"image_modifier": {
   "increase_nsb": true,
   "extra_noise_in_dim_pixels": 31.232,
   "extra_bias_in_dim_pixels": 6.977,
   "transition_charge": 8,
   "extra_noise_in_bright_pixels": 85.884,
   "increase_psf": false,
   "smeared_light_fraction": 0
 }

• run9605

"image_modifier": {
   "increase_nsb": true,
   "extra_noise_in_dim_pixels": 33.304,
   "extra_bias_in_dim_pixels": 7.233,
   "transition_charge": 8,
   "extra_noise_in_bright_pixels": 94.252,
   "increase_psf": false,
   "smeared_light_fraction": 0
 }

• run9606

"image_modifier": {
   "increase_nsb": true,
   "extra_noise_in_dim_pixels": 33.860,
   "extra_bias_in_dim_pixels": 7.2195,
   "transition_charge": 8,
   "extra_noise_in_bright_pixels": 100.024,
   "increase_psf": false,
   "smeared_light_fraction": 0
 }

• run9607

"image_modifier": {
   "increase_nsb": true,
   "extra_noise_in_dim_pixels": 37.096,
   "extra_bias_in_dim_pixels": 7.972,
   "transition_charge": 8,
   "extra_noise_in_bright_pixels": 107.688,
   "increase_psf": false,
   "smeared_light_fraction": 0
 }

DL1 data

• original DL1a files

/fefs/aswg/workspace/franca.cassol/data/real/DL1/20221010/tailcut84/
/fefs/aswg/data/mc/DL1/AllSky/20221027_v0.9.9_base_prod/*

• Produced DL1b data (2022-10-10)

/fefs/aswg/workspace/kenta.terauchi/Work/lst-analysis/source/GRB221009A/2022_10_10/DL1_new_calib_tuned/dl1_LST-1.Run0960[2-7].*.h5
/fefs/aswg/workspace/kenta.terauchi/Work/lst-analysis/NSB_tuning/AllSky_nsb_tuned/run960[2-7]/*

• Intensity distributions

• 

  Intensity distributions on 2022-10-10. The distributions are not normalized.

Random forest

• lstchain-0.9.10, lstchain-0.9.14-dev (branch: extra_srcdep_params)

• source-dep (diffuse gamma, src_r<1deg)

/fefs/aswg/workspace/kenta.terauchi/Work/lst-analysis/RF/AllSky_nsb_tuned/run960[2-7]/trained_models_src_r_0-1/*

• source-dep (diffuse gamma, src_r<1deg, w/ extra srcdep parameters)

/fefs/aswg/workspace/kenta.terauchi/Work/lst-analysis/RF/AllSky_nsb_tuned/run960[2-7]/trained_models_src_r_0-1_extra_params/*

• source-dep (diffuse gamma, 0.2<src_r<0.6deg)

/fefs/aswg/workspace/kenta.terauchi/Work/lst-analysis/RF/AllSky_nsb_tuned/run960[2-7]/trained_models_src_r_02-06/*

• source-dep (diffuse gamma, 0.2<src_r<0.6deg, w/ extra srcdep parameters)

/fefs/aswg/workspace/kenta.terauchi/Work/lst-analysis/RF/AllSky_nsb_tuned/run960[2-7]/trained_models_src_r_02-06_extra_params/*

DL2 data

• lstchain-0.9.10, lstchain-0.9.14-dev (branch: extra_srcdep_params)

• Produced DL2 data (2022-10-10)

/fefs/aswg/workspace/kenta.terauchi/Work/lst-analysis/source/GRB221009A/2022_10_10/DL2_new_calib_tuned*/dl2_LST-1.Run0960[2-7].*.h5
/fefs/aswg/workspace/kenta.terauchi/Work/lst-analysis/NSB_tuning/AllSky_nsb_tuned/run960[2-7]/*

Cut optimization

I optimized cut values using moon Crab data (2022-11-05; run10341 - 10346) taken under similar conditions to the GRB data. The GRB run-wise MCs which best match the Crab data (in terms of NSB level and Zd/Az range) are applied. The cut optimization procedure is as follows:

1. Get Non, Noff and normalization with a certain set of gammaness and alpha cuts
2. Replace Nex with 0.05*Nex (5% Crab sensitivity) and recalculate Li&Ma significance (For the calculation of excess and LiMa, I used WStatCountsStatistic class in gammapy)
3. Search for cuts value which give the maximum Li&Ma significance

• Results

• 

  Results for cut optimization; RF trained with src_r: 0.2-0.6; Extra srcdep parameters for g/h separation are used; Intensity > 300
• 

  Results for cut optimization; RF trained with src_r: 0-1.0; Extra srcdep parameters for g/h separation are used; Intensity > 300

Alpha plot

• 3 OFF regions
• Intensity > 300
• Normalization range: 40 < alpha(deg) < 80
• Used optimized cuts

• 2022-10-10

• 

  Alpha plot; RF trained with src_r: 0.2-0.6; Extra srcdep parameters for g/h separation are used
• 

  Alpha plot; RF trained with src_r: 0-1.0; Extra srcdep parameters for g/h separation are used

DL3 data selection

Information about your DL3 data selection.

Example

• intensity > 50
• r: [0, 1 ]
• wl: [0.1, 1 ]
• leakage_intensity_width_2: [0, 0.2 ]

• source-indep
  • fixed_gh_cut: 0.3
  • fixed_theta_cut: 0.2

• source-dep
  • fixed_gh_cut: 0.7
  • fixed_alpha_cut: 10

High-level analysis

Please put any information about the production of higher level analysis here.

Example

• lstchain to generate source-dep IRF, DL3
• Science Tool: gammapy 0.18.2
• point-like IRF, 1D analysis

Analysis Results

Please place higher-level analysis results (Spectra, SkyMaps, Lightcurves, etc) here.