| Name: | Dimple Dimple |
| Affiliation: | CMI, India |
| Conference ID : | ASI2024_835 |
| Title : | Evidence for two distinct populations of kilonova‑associated Gamma‑Ray Bursts using machine learning algorithms |
| Authors : | Dimple, Kuntal Misra, K. G. Arun |
| Authors Affiliation: | Dimple (CMI), Kuntal Misra (ARIES), K. G. Arun (CMI) |
| Mode of Presentation: | Poster |
| Abstract Category : | High Energy Phenomena, Fundamental Physics and Astronomy |
| Abstract : | Identification of gamma-ray burst (GRB) progenitors based on the duration of their prompt emission (T90) has faced several roadblocks recently. Long-duration GRBs (with T90 > 2 s) have traditionally been thought to be originating from the collapse of massive stars and the short-duration ones (with T90 < 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. |
