Distinguishing short and long $Fermi$ gamma-ray bursts

TL;DR

This study uses machine learning with Hurst Exponents, minimum variability time-scales, and durations to improve the classification accuracy of short and long gamma-ray bursts, potentially leading to clearer distinctions.

Contribution

It introduces a novel classification approach combining multiple parameters with machine learning to better distinguish GRB types.

Findings

Support Vector Machine achieves higher accuracy with combined parameters.

Hurst Exponents and variability time-scales improve classification success.

Proposes a new non-linear parameter for GRB classification.

Abstract

Two classes of gamma-ray bursts (GRBs), short and long, have been determined without any doubts, and are usually ascribed to different progenitors, yet these classes overlap for a variety of descriptive parameters. A subsample of 46 long and 22 short $Fermi$ GRBs with estimated Hurst Exponents (HEs), complemented by minimum variability time-scales (MVTS) and durations ($T_{90}$) is used to perform a supervised Machine Learning (ML) and Monte Carlo (MC) simulation using a Support Vector Machine (SVM) algorithm. It is found that while $T_{90}$ itself performs very well in distinguishing short and long GRBs, the overall success ratio is higher when the training set is complemented by MVTS and HE. These results may allow to introduce a new (non-linear) parameter that might provide less ambiguous classification of GRBs.