Deep Neural Networks for Estimation of Gamma-Ray Burst Redshifts

Abstract

While the available set of Gamma-ray Burst (GRB) data with known redshift is currently limited, a much larger set of GRB data without redshift is available from different instruments. This data includes well-measured prompt gamma-ray flux and spectral information. We estimate the redshift of a selection of these GRBs detected by Fermi-GBM and Konus-Wind using Machine Learning techniques that are based on spectral parameters. We find that Deep Neural Networks with Random Forest models employing non-linear relations among input parameters can reasonably reproduce the pseudo-redshift distribution of GRBs, mimicking the distribution of GRBs with spectroscopic redshift. Furthermore, we find that the pseudo-redshift samples of GRBs satisfy (i) Amati relation between the peak photon energy of the time-averaged energy spectrum in the cosmological rest frame of the GRB $E_{\mathrm{i},\mathrm{p}}$ and the isotropic-equivalent radiated energy $E_{\mathrm{iso}}$ during the prompt phase; and (ii) Yonetoku relation between $E_{\mathrm{i},\mathrm{p}}$ and isotropic-equivalent luminosity $L_{\mathrm{iso}}$, both measured during the peak flux interval.

10 pages, 7 figures, accepted in MNRAS

References in corpus (12)

• Cosmology with gamma-ray bursts: I. The Hubble diagram through the calibrated $E_{\mathrm{p},\mathrm{i}}$ - $E_{\mathrm{iso}}$ correlation
• The Epeak-Eiso plane of long Gamma Ray Bursts and selection effects
• Generalized Tests for Selection Effects in GRB High-Energy Correlations
• Model independent calibrations of gamma ray bursts using machine learning
• The Konus-Wind catalog of gamma-ray bursts with known redshifts. II. Waiting mode bursts simultaneously detected by Swift/BAT
• Normalization Techniques in Training DNNs: Methodology, Analysis and Application
• Variation of the Amati Relation with the Cosmological Redshift: a Selection Effect or an Evolution Effect?
• Revisiting the Amati and Yonetoku Correlations with Swift GRBs
• Empirical analysis of non-linear activation functions for Deep Neural Networks in classification tasks
• Selection effects on GRB spectral-energy correlations
• Gamma-ray Bursts as distance indicators through a machine learning approach
• Confirmed width-Eiso and width-Liso relations in GRB: comparison with the Amati and Yonetoku relations

Cited by in corpus (3)

• On the Feasibility of Deriving Pseudo-Redshifts of Gamma-ray Bursts from Two Phenomenological Correlations
• Estimating the peak energy of Swift gamma-ray bursts using supervised machine learning
• Do the Amati and Yonetoku Relations Evolve with Redshift for Swift GRBs?