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

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.