Gamma-ray Bursts as distance indicators through a machine learning approach

TL;DR

This paper demonstrates that machine learning can accurately infer gamma-ray burst redshifts from observed features, making GRBs effective distance indicators for cosmological studies.

Contribution

The study introduces a machine learning approach that significantly improves redshift predictions of GRBs, enhancing their utility as distance indicators in cosmology.

Findings

High correlation coefficient (0.96) between inferred and observed redshifts.

Mean square error of 0.003 in redshift prediction.

Plateau afterglow parameters improve predictions by 61.4%.

Abstract

Gamma-ray bursts (GRBs) are spectacularly energetic events, with the potential to inform on the early universe and its evolution, once their redshifts are known. Unfortunately, determining redshifts is a painstaking procedure requiring detailed follow-up multi-wavelength observations often involving various astronomical facilities, which have to be rapidly pointed at these serendipitous events. Here we use Machine Learning algorithms to infer redshifts from a collection of observed temporal and spectral features of GRBs. We obtained a very high correlation coefficient ($0.96$) between the inferred and the observed redshifts, and a small dispersion (with a mean square error of $0.003$) in the test set. The addition of plateau afterglow parameters improves the predictions by $61.4\%$ compared to previous results. The GRB luminosity function and cumulative density rate evolutions, obtained from predicted and observed redshift are in excellent agreement indicating that GRBs are effective distance indicators and a reliable step for the cosmic distance ladder.