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arXiv · 2304.08666

Identification of Extended Emission Gamma-Ray-Bursts Candidates using Machine Learning

K. Garcia-Cifuentes·R. L. Becerra·F. De Colle·J. I. Cabrera·C. del Burgo
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Abstract

Gamma-Ray bursts (GRBs) have been traditionally classified based on their duration. The increasing number of extended emission (EE) GRBs, lasting typically more than 2 seconds but with properties similar to those of a short GRBs, challenges the traditional classification criteria. In this work, we use the t-Distributed Stochastic Neighbor Embedding (t-SNE), a machine learning technique, to classify GRBs. We present the results for GRBs observed until July 2022 by the Swift/BAT instrument in all its energy bands. We show the effects of varying the learning rate and perplexity parameters as well as the benefit of pre-processing the data by a non-parametric noise reduction technique FABADA. Consistently with previous works, we show that the t-SNE method separates GRBs in two subgroups. We also show that EE GRBs reported by various authors under different criteria tend to cluster in a few regions of our t-SNE maps, and identify seven new EE GRB candidates by using the gamma-ray data provided by the automatic pipeline of Swift/BAT and the proximity with previously identified EE GRBs.

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K. Garcia-Cifuentes, R. L. Becerra, F. De Colle, J. I. Cabrera, C. del Burgo. 2023-04-28. Identification of Extended Emission Gamma-Ray-Bursts Candidates using Machine Learning. https://doi.org/10.3847/1538-4357%2Facd176

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