Machine-Learning Enhanced Photometric Analysis of the Extremely Bright GRB 210822A
Camila Angulo-Valdez, Rosa L. Becerra, Margarita Pereyra, Keneth, Garcia-Cifuentes, Felipe Vargas, Alan M. Watson, Fabio De Colle, Nissim, Fraija, Nathaniel R. Butler, Maria G. Dainotti, Simone Dichiara, William H., Lee, Eleonora Troja, Joshua S. Bloom, J. Jes\'us Gonz\'alez

TL;DR
This paper combines analytical, numerical, and machine learning models to analyze the bright GRB 210822A, providing insights into its emission mechanisms, jet structure, and physical parameters using multi-wavelength data.
Contribution
It introduces a machine-learning approach with AFTERGLOWPY to model GRB light curves and estimates key physical parameters of GRB 210822A, advancing the analysis of bright GRBs.
Findings
Consistent forward and reverse shock emission models.
Identification of a jet break constraining jet opening angle.
Machine learning estimates of physical parameters with uncertainties.
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,…
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Taxonomy
TopicsGamma-ray bursts and supernovae
