Machine learning and Gamma-Ray Bursts

Abstract:

Gamma-ray bursts (GRBs) can be probes of the early Universe, but currently, only 26% of GRBs observed by the Neil Gehrels Swift Observatory have known redshifts (z) due to observational limitations. To address this, we estimated the GRB redshift (distance) via a supervised statistical learning model (the Superlearner) that uses optical afterglow observed by Swift and ground-based telescopes. The inferred redshifts are strongly correlated (a Pearson coefficient of 0.93) with the observed redshifts, thus proving the reliability of this method. The inferred and observed redshifts allow us to estimate the number of GRBs occurring at a given redshift (GRB rate) to be 8.47–9 yr‑1 Gpc‑1 for 1.9 < z < 2.3. Since GRBs come from the collapse of massive stars, we compared this rate with the star formation rate, highlighting a discrepancy of a factor of 3 at z < 1. I will also discuss a powerful method again the Superlearner, which combines several machine learning models, for the redshift classifier for classifying GRBs in low-z and high-z (closer or faraway). In order to refine these methods, we need to have more precise parameter estimation of the parameters of the lightcurve to avoid gaps in the data. Thus, we reconstruct the lightcurves with Gaussian processes, Long Short Term memory, Bi-Mamba and other methods.