BLLac2022flare

BL Lacertae 2022 flare project

Major flare from BL Lacertae between September and November 2022, one of the (if not the most) longest VHE flare observed from the source, and the brightest together with the 2021 flare.

We aim to characterize the VHE and MWL variability during the flare, both in flux and spectral variability, with focus on the intranight minute timescale variations in the VHE band, and to perform a sort of time dependent modelling of the emission to interpret the evolution of the flare in terms of physical mechanisms and paramters.

General information

• Name of the source: BL Lacertae
• Brief description of the source:

- Object type : Blazar

- RA: 22 02 43.3 (hh mm ss), Dec: +42 16 40 (dd mm ss)

• Analysis by:
  • Jorge Otero Santos (IAA-CSIC, joteros@iaa.es)
  • Daniel Morcuende (IAA-CSIC, dmorcuende@iaa.es)

Data-taking information

• Dates of data-taking:
  • Date: 16/09/2022-25/11/2022 (53 h before cuts)
    • ZD range (deg): 15-60 deg
    • AZ range (deg): 0-60 and 300-360 deg
    • Joint observations with MAGIC? : No
    • Runs:

1 : 2022-09-16 : 9228 to 9232
2 : 2022-09-21 : 9262 to 9268
3 : 2022-09-22 : 9347 to 9350
4: 2022-10-01: 9453 to 9461
5: 2022-10-03: 9544 to 9555
6: 2022-10-15: 9631 to 9635
7: 2022-10-16: 9648
8: 2022-10-17: 9678 to 9685
9: 2022-10-18: 9697 to 9706
10: 2022-10-19: 9729 to 9739
11: 2022-10-20: 9753 to 9761
12: 2022-10-21: 9808 to 9815
13: 2022-10-23: 9836 to 9848
14: 2022-10-24: 9864 to 9873
15: 2022-10-25: 9892 to 9900
16: 2022-10-26: 9921 to 9924
17: 2022-10-27: 9940 to 9944
18: 2022-10-28: 9969 to 9973
19: 2022-10-29: 9993 to 9995
20: 2022-10-31: 10022 to 10029
21: 2022-11-01: 10069 to 10076
22: 2022-11-02: 10193 to 10204
23: 2022-11-13: 10508 to 10512
24: 2022-11-14: 10521 to 10523
25: 2022-11-15: 10586 to 10588
26: 2022-11-16: 10616 to 10620
27: 2022-11-17: 10652 to 10655
28: 2022-11-18: 10827 to 10830
29: 2022-11-19: 10870 to 10872
30: 2022-11-25: 11033 to 11037
 

Data-quality selection

Used latest version of the data quality selection notebook.

Standard configuration + dR/dI cosmics rate @ 422 p.e. = 1.1

Cut of max_diffuse_nsb_std = 2.3 for moon data selection

720px

• Selected dark runs: 104 of 193 runs (29 h)

1 : 2022-09-21 : 9262 to 9268
2: 2022-10-01: 9459 to 9461
3: 2022-10-03: 9544
4: 2022-10-16: 9648
5: 2022-10-17: 9678 to 9683
6: 2022-10-18: 9698 to 9705
7: 2022-10-19: 9730 to 9739
8: 2022-10-20: 9753 to 9761
9: 2022-10-21: 9809 to 9814
10: 2022-10-23: 9843, 9844, 9845, 9846, 9848
11: 2022-10-24: 9866
12: 2022-10-25: 9892 to 9900
13: 2022-10-26: 9921 to 9924
14: 2022-10-27: 9940 to 9944
15: 2022-10-29: 9993 to 9995
16: 2022-10-31: 10025, 10026, 10028, 10029
17: 2022-11-13: 10508
18: 2022-11-15: 10586 to 10587
19: 2022-11-16: 10616 to 10620
20: 2022-11-17: 10652 to 10655
21: 2022-11-18: 10827 to 10830
22: 2022-11-19: 10871 to 10872
23: 2022-11-25: 11033 to 11037
 

• Selected moon runs: 30 of 193 runs (8 h)

1: 2022-10-01: 9453 to 9457
2: 2022-10-03: 9544, 9545, 9546, 9548, 9549, 9550, 9551, 9552
3: 2022-10-15: 9631, 9633, 9634, 9635
4: 2022-10-17: 9684
5: 2022-10-31: 10022 to 10023
6: 2022-11-01: 10071, 10072, 10073, 10074, 10076
7: 2022-11-13: 10509 to 10512
8: 2022-11-14: 10523
 

Monte Carlo information

• Link to MC files used:
  • Dark data: /fefs/aswg/data/mc/DL2/AllSky/20230925_v0.10.4_src3_dec3476_4822_tuned/TestingDataset/dec_3476/
  • Moon data: /fefs/aswg/data/mc/DL2/AllSky/20240426_v0.10.9_src3_dec3476_4822_tunedNSB/TestingDataset/dec_3476/ (tuned NSBs and cleaning to match that needed for the real data)

DL1 data

Dark data :

Standard data from /fefs/aswg/data/real/DL1/ with version v0.9 and cleaning tailcut84

Moon data :

Moon data reprocessed to produce the DL1b files with the optimal cleaning for moon analysis as calculated with lstchain_dvr_pixselector -f "/fefs/aswg/data/real/DL1/2022XXXX/v0.9/tailcut84/dl1_LST-1.RunXXXXX'.*.h5" Reprocessed using DL1a files produced by LSTOSA (lstchain v0.10.XX) and dl1ab script (v0.7.5)

• original DL1a files

/fefs/aswg/data/real/DL1/2022XXXX/v0.9/tailcut84/dl1_LST-1.Run0XXXX.XXXX.h5

SPECIFY THE CONFIGURATION FOR OPTIMIZING THE CLEANING

• Produced DL1b files

/fefs/aswg/workspace/daniel.morcuende/data/real/DL1b/BLLac_2022_NSB/tailcut_*_*/merged

 tailcut105: 09454, 09455, 09456, 09457, 09631, 09684
 tailcut126: 09453, 09551, 09552, 10022, 10023, 10509, 10523
 tailcut147: 09546, 09548, 09549, 09550, 09633, 09634, 09635, 10071, 10072, 10073, 10074, 10076, 10510, 10511
 tailcut168: 09544, 09545, 10512
 

Low-level analysis

• Interpolated IRFs
• Custom MC and models (as explained above for dark/moon data)
• Standard 70% efficiency energy dependent cuts
• Max theta cut = 0.2
• Intensity dark = 50
• Intensity moon = TBD

High-level analysis

• lstchain v0.10.11 to generate source-indep interpolated IRF, energy dependent cuts DL3
• Science Tool: gammapy 1.1
• point-like IRF, 1D analysis
• Aeff>5%
• 3OFF

Energy threshold

Dark data :

Estimated from the dark MCs.

Moon data :

Estimated from the moon MCs.

Jorge's Analysis Results

We produce the stacked and daily theta2 plots and average SED.

With the average SED, a preliminary LC is also computed and a Bayesian Block analysis. We then calculate the SED of each BB to look for spectral variability.

For a more precise LC, we then use the SED of each block to calculate the LC block by block to build the long-term final LC. This is to avoid miscalculations in the integral flux caused by using the wrong spectral shape if there is strong spectral variability.

We take as reference the energy threshold estimated from the moon MCs for the minimum energy in which we integrate the flux for the LC. This energy threshold is specified above.

Configuration of the analysis:

• E_reco=[XX,XX] GeV
• E_true=[XX,XX] GeV
• n_bins=XX
• E_fit=[XX,XX] GeV
• Spectral shape: Log parabola+EBL (Saldaña-Lopez21, z=0.069)
• Analysis type: stack
• LC energy=[100,XX] GeV

The rest of the details of the high-level analysis are as specified above.

Daniel's Analysis Results

Relevant details