Cosmological Evolution of Gamma Ray Bursts

TL;DR

This study investigates the evolution of gamma-ray bursts (GRBs) over cosmic time, using an expanded dataset and machine learning to analyze their redshift distribution and luminosity functions, revealing a low-redshift excess of long GRBs.

Contribution

It introduces a larger, more comprehensive GRB sample including ML-estimated redshifts and applies advanced statistical methods to analyze their evolution and progenitor models.

Findings

Confirmed low-redshift excess of long GRBs in larger sample

ML-estimated redshifts reduce observational bias

Results support possible merger progenitors for some low-redshift LGRBs

Abstract

Gamma Ray Bursts (GRBs) are classified as long (LGRBs) and short (SGRBs) with collapsars and compact object mergers (neutron star (NS)-NS or NS-Black hole) as progenitors, respectively. The former are expected to follow the cosmic star formation rate (SFR), while the latter follows a delayed version of the SFR. However, this division has come under question in several ways, the most prominent being the observational evidence of a significant excess of LGRBs at low redhifts by several independent investigations, summarized in arXiv:2305.15081. This could indicate that the progenitors of low-redshift LGRBs, whose formation rates are delayed, (similar to that of SGRBs) are compact mergers rather than collapsars. Two recent observations of low-redshift LGRBs show associations with kilonovae, a clear feature of compact mergers. Most results showing this separation are based on analyses of small (less than 200) samples of LGRBs with measured redshifts. The aim of this paper is to use a larger sample of LGRBs. The number of LGRBs with measured redshifts has increased by more than a factor of 2 over the last decade. To this data set we add a sample of LGRBs whose redshifts are estimated using a machine learning (ML) method (arXiv:2410.13985). This, in addition to increasing the sample size, reduces the observational selection bias arising from the process of redshift measurement. To account for this bias, we use the non-parametric, non-binning Efron-Petrosian method to establish the degree of correlation between luminosity and redshift, the luminosity evolution, which then allows us to use the Lynden-Bell $C^-$ method to obtain a complete description of the luminosity function. We find similar low redshift excess for the larger sample with measured redshifts. Adding the sources with ML-estimated redshifts, which tend to have more sources in mid-range redshifts, the excess is reduced.