Gamma-ray burst light curve reconstruction with predictive models

TL;DR

This paper introduces a machine learning framework using recurrent neural networks to accurately reconstruct gamma-ray burst light curves, especially during the plateau phase, improving understanding of these energetic cosmic events.

Contribution

It compares three sequential models and demonstrates that the Bidirectional Gated Recurrent Unit outperforms others in reconstructing complex gamma-ray burst light curves.

Findings

Bidirectional Gated Recurrent Unit achieved the highest predictive accuracy.

The model effectively captures both gradual and abrupt features in light curves.

Reconstruction accuracy was validated across various gamma-ray burst types.

Abstract

Gamma-ray bursts represent some of the most energetic and complex phenomena in the universe, characterized by highly variable light curves that often contain observational gaps. Reconstructing these light curves is essential for gaining deeper insight into the physical processes driving such events. This study proposes a machine learning-based framework for the reconstruction of gamma-ray burst light curves, focusing specifically on the plateau phase observed in X-ray data. The analysis compares the performance of three sequential modeling approaches: a bidirectional recurrent neural network, a gated recurrent architecture, and a convolutional model designed for temporal data. The findings of this study indicate that the Bidirectional Gated Recurrent Unit model showed the best predictive accuracy among the evaluated models across all GRB types, as measured by Mean Absolute Error, Root Mean Square Error, and Coefficient of Determination. Notably, Bidirectional Gated Recurrent Unit exhibited enhanced capability in modeling both gradual plateau phases and abrupt transient features, including flares and breaks, particularly in complex light-curve scenarios.