Gamma-ray burst redshift estimation using machine learning and the associated web app

¹ Astronomical Observatory of Jagiellonian University in Kraków, Orla 171, 30-244 Kraków, Poland
² Jagiellonian University, Doctoral School of Exact and Natural Sciences, Krakow, Poland
³ Division of Science, National Astronomical Observatory of Japan, 2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan
⁴ The Graduate University for Advanced Studies (SOKENDAI), Shonankokusaimura, Hayama, Miura District, Kanagawa 240-0115, Japan
⁵ Space Science Institute, 4765 Walnut St Ste B, Boulder, CO 80301, USA
⁶ Nevada Center for Astrophysics, University of Nevada, 4505 Maryland Parkway, Las Vegas, NV 89154, USA
⁷ Bay Environmental Institute, PO Box 25, Moffett Field, CA, USA
⁸ National Center for Nuclear Physics (NCBJ), Warsaw, Poland
⁹ Department of Physical Sciences, Indian Institute of Science Education and Research (IISER), Mohali, Punjab, India
¹⁰ Department of Physics and Kavli Institute of Particle Astrophysics and Cosmology, Stanford University, Stanford, CA 94305, USA
¹¹ University of Nevada, Las Vegas, 4505 S. Maryland Pkwy, Las Vegas, NV 89154, USA
¹² Department of Mathematics, University of Wroclaw, 50-384 Wrocław, Poland
¹³ Department of Statistics, Lund University, 221 00 Lund, Sweden
¹⁴ Center for Computational Astrophysics, National Astronomical Observatory of Japan, 2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan

^★★ Corresponding author: maria.dainotti@nao.ac.jp

Received: 17 October 2024
Accepted: 5 April 2025

Abstract

Context. Gamma-ray bursts (GRBs), which have been observed at redshifts as high as 9.4, could serve as valuable probes for investigating the distant Universe. However, using them in this manner necessitates an increase in the number of GRBs with determined redshifts, as currently only 12% of them have known redshifts due to observational biases.

Aims. We aim to address the shortage of GRBs with measured redshifts to enable full realization of their potential as valuable cosmological probes.

Methods. Following our previous approach, in this work we take a further step to overcome this issue by adding 30 more GRBs to our ensemble supervised machine learning training sample, representing an increase of 20%, which will help us obtain more accurate pseudo-redshifts. In addition, we have built a freely accessible and user-friendly web application that infers the redshift of long GRBs (LGRBs) with plateau emission using our machine learning model. The web app is the first of its kind for such a study and will allow the community to obtain pseudo-redshifts by entering the GRB parameters into the app.

Results. Through our machine learning model, we successfully estimated redshifts for 276 LGRBs using X-ray afterglow parameters detected by the Neil Gehrels Swift Observatory and increased the sample of LGRBs with known redshifts by 110%. We also performed Monte Carlo simulations to demonstrate the future applicability of this research.

Conclusions. The results presented in this work will enable the community to increase the sample of GRBs with known pseudoredshifts. This can help address many outstanding issues, such as GRB formation rate, luminosity function, and the true nature of low-luminosity GRBs, and it can enable the application of GRBs as standard candles.