Evidence for Two Distinct Populations of Kilonova-associated Gamma-Ray Bursts

Abstract

Identification of gamma-ray burst (GRB) progenitors based on the duration of their prompt emission (T 90) has faced several roadblocks recently. Long-duration GRBs (with T 90 > 2 s) have traditionally been thought to be originating from the collapse of massive stars and the short-duration ones (with T 90 < 2 s) from compact binary mergers. However, recent observations of a long GRB associated with a kilonova (KN) and a short GRB with supernova association demand a more detailed classification of the GRB population. In this Letter, we focus on GRBs associated with KNe, believed to be originating from mergers of binaries involving neutron stars (NSs). We make use of the GRB prompt-emission light curves of the Swift/BAT 2022 GRB catalog and employ machine-learning algorithms to study the classification of GRB progenitors. Our analysis reveals that there are five distinct clusters of GRBs, of which the KN-associated GRBs are located in two separate clusters, indicating they may have been produced by different progenitors. We argue that these clusters may be due to subclasses of binary neutron star and/or NS–black hole mergers. We also discuss the implications of these findings for future gravitational-wave observations and how those observations may help in understanding these clusters better.

Multi-messenger astrophysics of black holes and neutron stars as probed by ground-based gravitational wave detectors: from present to future

“…In fact, this classification scheme has been challenged by observations of long GRBs associated with kilonovae or lacking supernova counterparts to very deep limits, and short GRBs showing potential supernova bumps in their light curves (Della Valle et al, 2006;Fynbo et al, 2006;Ahumada et al, 2021;Troja et al, 2022a;Rastinejad et al, 2022;Rossi et al, 2022;Yang et al, 2022;Barnes and Metzger, 2023;Gompertz et al, 2023b). In the future, deep GW observations of these peculiar GRBs will provide the definitive word on the nature of their progenitors and likely settle current classification debates (Dimple et al, 2023).…”

Significant Cocoon Emission and Photosphere Duration Stretching in GRB 211211A: A Burst from a Neutron Star−Black Hole Merger

“…Surprisingly, the kilonova signal is discovered in the late-time (∼days) afterglow-subtracted optical-near-IR (NIR) data (Rastinejad et al 2022;Troja et al 2022;Yang et al 2022). This significant conflict draws much attention to exploring its origin (NS-WD, Yang et al 2022;Zhong et al 2023;NS-BH, Zhu et al 2022; see also Dimple et al 2023;Gao et al 2022;Mei et al 2022;Waxman et al 2022;Barnes & Metzger 2023;Chang et al 2023;Yin et al 2023;Zhou et al 2024;Kunert et al 2024). In this work, we discover the evidence that it (and other short GRBs with EE) comes from the NS-BH merger (with intrinsic duration ∼3 s).…”

Exploring Gamma-Ray Burst Diversity: Clustering Analysis of the Emission Characteristics of Fermi- and BATSE-detected Gamma-Ray Bursts

“…However, a significant number of GRBs are found to possess temporal and spectral properties that do not fall into these conventional criteria for classes of progenitors based on their T 90 measurements. For example, GRB 200826A Rossi et al 2022) is a short-duration GRB but is associated with stellar core-collapse origin; on the other hand, GRB 211211A (Troja et al 2022;Yang et al 2022) and GRB 230307A (Dichiara et al 2023;Levan et al 2023) are long-duration GRBs associated with the detection of kilonovae (Dimple et al 2023). Furthermore, there have been long-duration GRBs, such as GRBs 060614 and 060505 (Della Valle et al 2006;Fynbo et al 2006), where no supernovae were observed to accompany the GRBs.…”

“…These attempts were made to identify classes in GRBs based on the prompt emission parameters such as duration, hardness ratio, fluences, etc. (Rajaniemi & Mahonen 2002;Hakkila et al 2003) as well as using the morphology of the GRB prompt emission light curves (Jespersen et al 2020;Dimple et al 2023;Garcia-Cifuentes et al 2023;Steinhardt et al 2023), using various machine-learning techniques of unsupervised clustering. Furthermore, we note that parameters such as redshift, kilonova detection, supernova detection, host galaxy information, burst energetics, afterglow properties, and gravitational wave detections, etc.…”