Distinguishing short and long $Fermi$ gamma-ray bursts
This work addresses classification ambiguity in gamma-ray bursts for astrophysics research, but it is incremental as it builds on existing parameters with minor enhancements.
The study tackled the problem of distinguishing short and long gamma-ray bursts (GRBs) by using a supervised machine learning approach with Support Vector Machine (SVM) on a dataset of 46 long and 22 short Fermi GRBs, finding that complementing duration with minimum variability time-scales and Hurst Exponents improved the overall success ratio compared to using duration alone.
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.