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

Kavli Affiliate: Vahe Petrosian

| First 5 Authors: Maria Giovanna Dainotti, Aditya Narendra, Agnieszka Pollo, Vahe Petrosian, Malgorzata Bogdan

| Summary:

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 machine 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 7.6-8 $yr^{-1} Gpc^{-1}$ 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 $z<1$.

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