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Gamma-ray bursts (GRB) are powerful transient events that emit a large outputof gamma rays within a few seconds. Studying these short bursts is vital forcosmological research since they originate from sources observed at largeredshifts. To effectively carry out these studies, it is crucial to establish acorrelation between the observable features of GRBs while reducing theiruncertainty. For these reasons, a comprehensive description of the general GRBlight curve (LC) would be crucial for the studies. However, unevenly spacedobservations and significant gaps in the LC, which are primarily unavoidablefor various reasons, make it difficult to characterize GRBs. Therefore, thegeneral classification of GRB LCs remains challenging. In this study, wepresent a novel approach to reconstruct gamma-ray burst (GRB) light curvesusing bidirectional Long Short-Term Memory (BiLSTM). Experimental results showthat the BiLSTM approach performs better than traditional methods and producessmoother and more convincing reconstructions for GRBs.

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