Characterization and classification of $\gamma$-ray bursts from blazars

TL;DR

This study provides a comprehensive analysis and classification of gamma-ray bursts from blazars, revealing a mostly homogeneous population with distinct achromatic and chromatic classes, and identifies correlations with luminosity and timescales.

Contribution

It introduces a population-level characterization and classification of GRBBLs using machine learning, which was not previously done.

Findings

GRBBL population is remarkably homogeneous.

Classified into achromatic and chromatic groups with a smooth transition.

Identified a correlation between burst timescales and peak luminosity.

Abstract

Blazars are the most common sources of $\gamma$-ray photons in the extragalactic sky. Their $\gamma$-ray light curves are characterized by bright flaring episodes, similarly to what is observed at longer wavelengths. These gamma-ray bursts from blazars (GRBBLs) have been extensively studied individually, but never in terms of a population. The goal of this work is to provide a global characterization of GRBBLs, to investigate the parameter space of the population, and ultimately to classify GRBBLs. Their global properties could give insights into the physical mechanisms responsible for the $\gamma$-ray radiation and into the origin of the observed variability. I analyzed a sample of publicly available Fermi-LAT light curves, utilizing only blazars with certain redshift measurements. The redshift-corrected light curves were then automatically scanned to identify GRBBLs. A simple flare profile, with an exponential rise and decay, was then fit to all events. The fit parameters, together with the information on spectral variability during the events, and the global properties from the LAT catalog, were then used as inputs for unsupervised machine learning classification. The analysis shows that the GRBBL population is remarkably homogeneous. The classifier splits the population into achromatic (the large majority) and chromatic (the outliers) GRBBLs, but the transition between the two classes is smooth, with significant overlap. When the information on the spectral variability is removed, there is evidence for a classification into two classes, mainly driven by the peak luminosities. As a by-product of this study, I identify a correlation between the timescales of the GRBBLs and their peak luminosity.