Machine-Learning Enhanced Photometric Analysis of the Extremely Bright GRB 210822A
Abstract
We present analytical and numerical models of the bright long GRB 210822A at . The intrinsic extreme brightness exhibited in the optical, which is very similar to other bright GRBs (e.g., GRBs 080319B, 130427A, 160625A 190114C, and 221009A), makes GRB 210822A an ideal case for studying the evolution of this particular kind of GRB. We use optical data from the RATIR instrument starting at s, with publicly available optical data from other ground-based observatories, as well as Swift/UVOT, and X-ray data from the Swift/XRT instrument. The temporal profiles and spectral properties during the late stages align consistently with the conventional forward shock model, complemented by a reverse shock element that dominates optical emissions during the initial phases ( s). Furthermore, we observe a break at s that we interpreted as evidence of a jet break, which constrains the opening angle to be about degrees. Finally, we apply a machine-learning technique to model the multi-wavelength light curve of GRB 210822A using the AFTERGLOWPY library. We estimate the angle of sight degrees, the energy ergs, the electron index , the thermal energy fraction in electrons and in the magnetic field , the efficiency , and the density of the surrounding medium .
Keywords
Cite
@article{arxiv.2309.10106,
title = {Machine-Learning Enhanced Photometric Analysis of the Extremely Bright GRB 210822A},
author = {Camila Angulo-Valdez and Rosa L. Becerra and Margarita Pereyra and Keneth Garcia-Cifuentes and Felipe Vargas and Alan M. Watson and Fabio De Colle and Nissim Fraija and Nathaniel R. Butler and Maria G. Dainotti and Simone Dichiara and William H. Lee and Eleonora Troja and Joshua S. Bloom and J. Jesús González and Alexander S. Kutyrev and J. Xavier Prochaska and Enrico Ramirez-Ruiz and Michael G. Richer},
journal= {arXiv preprint arXiv:2309.10106},
year = {2023}
}
Comments
Resubmitted to MNRAS after moderate revision, 12 pages, 6 figures