Cosmological Evolution of Gamma Ray Bursts

Abstract

Gamma-ray bursts (GRBs) are classified as long (LGRBs) and short (SGRBs), with collapsars and compact-object mergers (NS-NS or NS-Black Holes) as progenitors, respectively. LGRBs are expected to follow the cosmic star formation rate (SFR), while SGRBs follow a delayed version of the SFR. However, this division has come under question, most prominently by observational evidence of an excess of LGRBs at low redshifts by several investigations, summarized in \cite{Petrosian_2024}. Two recent observations of low-redshift LGRBs show associations with kilonovae. Both of these indicate compact mergers as a potential source of LGRBs as well. 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 (\cite{Narendra_2025}). To account for the observational selection bias due to redshift measurements, we use the non-parametric, non-binning Efron-Petrosian method to establish the degree of correlation between luminosity and redshift, \textit{the luminosity evolution}, and then use the Lynden-Bell $C^{-}$ method to obtain the luminosity function. We find a low redshift excess for the larger sample with measured redshifts. Adding the sources with ML-estimated redshifts, which shows overabundance of the mid-range redshifts, the excess is reduced.

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