Machine Learning Identification of Gravitationally Microlensed Gamma-Ray Bursts

Abstract

Gravitational microlensing of gamma-ray bursts (GRBs) provides a unique opportunity to probe compact dark matter and small-scale structures in the Universe. However, identifying such microlensed GRBs within large data sets is a significant challenge. In this study, we develop a machine learning (ML) approach to distinguish lensed GRBs from their nonlensed counterparts, using simulated light curves. A comprehensive data set is generated, comprising labeled light curves for both categories. Features are extracted using the Cesium package, capturing critical temporal properties of the light curves. Multiple ML models are trained on the extracted features, with Random Forest achieving the best performance, delivering an accuracy of 86% and an F1 score of 0.86 (0.87) for the nonlensed (lensed) class. This approach successfully demonstrates the potential of ML for identifying gravitational lensing in GRBs, paving the way for future observational applications.

Published at The Astrophysical Journal

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