Predicting Missing Light Curves of Gamma-Ray Bursts with Bidirectional-LSTM: An Approach for Enhanced Analysis

TL;DR

This paper introduces a bidirectional LSTM model to accurately reconstruct gamma-ray burst light curves, addressing challenges of uneven data and gaps, thereby improving analysis for cosmological research.

Contribution

The study presents a novel BiLSTM-based method for reconstructing GRB light curves, outperforming traditional techniques and handling data gaps effectively.

Findings

BiLSTM outperforms traditional methods in light curve reconstruction.

Reconstructed light curves are smoother and more convincing.

The approach enhances the analysis of GRBs for cosmological studies.

Abstract

Gamma-ray bursts (GRB) are powerful transient events that emit a large output of gamma rays within a few seconds. Studying these short bursts is vital for cosmological research since they originate from sources observed at large redshifts. To effectively carry out these studies, it is crucial to establish a correlation between the observable features of GRBs while reducing their uncertainty. For these reasons, a comprehensive description of the general GRB light curve (LC) would be crucial for the studies. However, unevenly spaced observations and significant gaps in the LC, which are primarily unavoidable for various reasons, make it difficult to characterize GRBs. Therefore, the general classification of GRB LCs remains challenging. In this study, we present a novel approach to reconstruct gamma-ray burst (GRB) light curves using bidirectional Long Short-Term Memory (BiLSTM). Experimental results show that the BiLSTM approach performs better than traditional methods and produces smoother and more convincing reconstructions for GRBs.