Kavli Affiliate: Vahe Petrosian
| First 5 Authors: Aditya Narendra, Maria Dainotti, Milind Sarkar, Aleksander Lenart, Malgorzata Bogdan
| Summary:
Context. Gamma-ray bursts (GRBs), observed at redshifts as high as 9.4, could
serve as valuable probes for investigating the distant Universe. However, this
necessitates an increase in the number of GRBs with determined redshifts, as
currently, only 12% of GRBs have known redshifts due to observational biases.
Aims. We aim to address the shortage of GRBs with measured redshifts, enabling
us to fully realize their potential as valuable cosmological probes Methods.
Following Dainotti et al. (2024c), we have taken a second step to overcome this
issue by adding 30 more GRBs to our ensemble supervised machine learning
training sample, an increase of 20%, which will help us obtain better redshift
estimates. In addition, we have built a freely accessible and user-friendly web
app 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 redshift estimates by entering the
GRB parameters in the app. Results. Through our machine learning model, we have
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 perform Monte Carlo simulations to
demonstrate the future applicability of this research. Conclusions. The results
presented in this research will enable the community to increase the sample of
GRBs with known redshift estimates. This can help address many outstanding
issues, such as GRB formation rate, luminosity function, and the true nature of
low-luminosity GRBs, and enable the application of GRBs as standard candles
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