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 z = 1.736. 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 T + 315.9 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 (T < 300 s). Furthermore, we observe a break at T = 80 000 s that we interpreted as evidence of a jet break, which constrains the opening angle to be about θj = (3–5) degrees. Finally, we apply a machine-learning technique to model the multiwavelength light curve of GRB 210822A using the afterglowpy library. We estimate the angle of sight θobs = (6.4 ± 0.1) × 10−1 degrees, the energy E0 = (7.9 ± 1.6) × 1053 erg, the electron index p = 2.54 ± 0.10, the thermal energy fraction in electrons ϵe = (4.63 ± 0.91) × 10−5 and in the magnetic field ϵB = (8.66 ± 1.01) × 10−6, the efficiency χ = 0.89 ± 0.01, and the density of the surrounding medium n0 = 0.85 ± 0.01 cm−3.

ClassiPyGRB: Machine Learning-Based Classification and Visualization of Gamma Ray Bursts using t-SNE

“…ClassiPyGRB is a Python 3 package that aims to solve these problems by providing a simple and intuitive interface to download, process, visualize, and classify the photometric data of GRBs from the Swift/BAT database. ClassiPyGRB can also been used to promptly find similar GRBs with some specific feature, such as a bright component (Angulo-Valdez et al, 2024). 2023) (see e.g., Figure 1).…”

“…Garcia-Cifuentes et al (2024). ClassiPyGRB: Machine Learning-Based Classification and Visualization of Gamma Ray Bursts using t-SNE.…”