7–12 Jul 2024
Aurum, the ‘Gabriele d’Annunzio’ University and ICRANet
Europe/Rome timezone

Gamma-ray Bursts as Distance Indicators by a Statistical Learning Approach

8 Jul 2024, 17:00
15m
Sala 17 (Aurum)

Sala 17

Aurum

Largo Gardone Riviera, Pescara, Italy
Invited talk in a parallel session Gamma-ray bursts and AGNs with machine learning Gamma-ray bursts and AGNs with machine learning

Speaker

Aditya Narendra (Jagiellonian University)

Description

Gamma-ray bursts (GRBs) can be probes of the early universe, but currently, only 26% of GRBs 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 of 0.93) with the observed redshifts, thus proving the reliability of this method. The inferred and observed redshifts allow us to estimate the number of GRBs occurring at a given redshift (GRB rate) to be 8.47-9 yr−1Gpc−1 for 1.9<z<2.3. Since GRBs come from the collapse of massive stars, we compared this rate with the star formation rate highlighting a discrepancy of a factor of 3 at z<1.

Primary author

Co-author

Aditya Narendra (Jagiellonian University)

Presentation materials

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