| Name: | Aditya Narendra |
| Affiliation: | Jagiellonian University |
| Conference ID: | ASI2025_96 |
| Title: | Redshift estimation of Gamma-ray Bursts using machine learning |
| Authors: | Aditya Narendra
|
| Authors Affiliation: | Jagiellonian University, Krakow, Poland |
| Mode of Presentation: | Poster |
| Abstract Category: | High Energy Phenomena, Fundamental Physics and Astronomy |
| Abstract: | Gamma-ray bursts (GRBs) can be probes ofthe early universe, but currently, only 26% ofGRBs observed by the Neil Gehrels Swift Observatory GRBs have known redshifts (z) due to observational limitations. To address this, we estimated the GRB redshift (distance) via a supervised statistical learning model that uses optical afterglow observed by Swift and ground-based telescopes. The inferred redshifts are strongly correlated (a Pearson coefficient of0.93) with the observed redshifts, thus proving the reliability ofthis method. The inferred and observed redshifts allow us to estimate the number ofGRBs occurring at a given redshift (GRB rate) to be 8.47-9 yr−1Gpc−1 for 1.9 |
