Crab Nebula Performance Paper Dataset[edit]

General information[edit]

Name of the source: Crab Nebula
Brief description of the source:
- Pulsar Wind Nebula
- z = 0
- RA: 5 34 31.94 (hh mm ss), Dec: +22 0 52.2 (dd mm ss)
- RA, Dec in deg (ICRS): 83.63308333, 22.015

Analysis by Chaitanya Priyadarshi ((IFAE - cpriyadarshi@ifae.es)

Data-taking information[edit]

Same dataset, as used for the LST Performance Paper

Monte Carlo information[edit]

For getting a closer data-MC comparison, we produce MC data along various sky positions (nodes) as a preliminary method to facilitate a better Random Forest model for the reconstruction and to interpolate IRFs from various sky positions to the target position of each Observation Run.

DL1 data[edit]

This includes whether you use LSTOSA, specific versions of lstchain, dllab scripts, cleaning levels, and calibration information.

DL1a files produced by LSTOSA (lstchain v0.9)

original real DL1a data

/fefs/aswg/data/real/DL1/{Date}/v0.9/tailcut84/

lstchain v0.9 tailcut8-4 (with cleaning based on pedestal RMS, dynamical cleaning)

 /fefs/aswg/data/real/DL1/20220204/v0.9/tailcut84/log/lstchain_config_v0.9.4_from2022onwards_heuristic.json

MC nodes: tailcut8-4 (with cleaning based on pedestal RMS), dynamic cleaning, NSB tuning

 /fefs/aswg/data/mc/DL1/AllSky/20221027_v0.9.9_crab_tuned/{Testing, Training}Dataset/

Random forest[edit]

Here we use the TrainingDataset nodes, selected along the declination path (or closer to) of the selected source (Crab Nebula here). The "dec2276" part in the production ID refers to the declination angle of +22.76 deg. In this new RF model production, we include zenith and azimuth pointing parameters as well.

lstchain v0.9.13dev source-independent, dynamic cleaning, NSB tuning

 /fefs/aswg/data/models/AllSky/20221027_v0.9.9_crab_tuned/

For IRF interpolation[edit]

Here we create an almost uniform grid of nodes of sky positions for TestingDataset MC, so as to facilitate IRF interpolation in the parameter space of cos zenith and the sin of angle (delta) between the geomagnetic field and the particle shower direction. IRF interpolation is performed using pyirf v0.7.

Link to MC DL2 files used:

/fefs/aswg/data/mc/DL2/AllSky/20221027_v0.9.9_crab_tuned/TestingDataset/

Particle types: ring-wobble gamma (offset range = [0.3999, 0.4001] deg)

DL2 data[edit]

The DL2 files of the real data are stored here - /fefs/aswg/workspace/chaitanya.priyadarshi/real_data/AGN/Crab/v09x/tailcut84/DL2/real/src_indep/20221027_v0.9.9_crab_tuned/{Date}

DL3 data selection[edit]

For energy-dependent cuts[edit]

Information about your DL3 data selection.

intensity > 50, 80
r: [0, 1]
wl: [0.01, 1]
leakage_intensity_width_2: [0, 1]

source-independent
- gh_efficiency: 0.8 (0.7 for comparison with Analysis in the Performance Paper)
- theta_containment: 0.8 (0.7 for comparison with Analysis in the Performance Paper)

Energy-dependent Gammaness and Theta cut distribution

High-level analysis[edit]

lstchain Tools to generate source-independent IRF and DL3
Science Tool: gammapy 1.0
point-like IRF, 1D analysis

For DL3 data reduction to DL4 dataset,

OFF regions selected - 1 Wobble
Excluded region: RGB J0521+212, circular region with radius 0.3 deg
Safe Energy mask for systematics - Yes, 25.1 GeV < E < 40 TeV
Energy axes -
- True: 10 GeV to 100 TeV with 10 bins per decade
- Reco: 10 GeV to 100 TeV with 10 bins per decade

For spectral analysis,

Model used - Log Parabola + EBL (Dominguez)
Reference energy used by calculating the decorrelation energy with a Power Law fit, 431 GeV (397 GeV for comparison with Analysis in the Performance Paper)
Spectral Fit energy range - 10 GeV to 100 TeV with 10 bins per decade
Spectral fit with gammapy default optimization - minuit

For LC,