Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample

2025/06/10 by Huang, Zhen, Luo, Xin, Zhang, Bin +4 · 1 citation
#Cosmology and Nongalactic Astrophysics (astro-ph.CO) #FOS: Physical sciences

paper · doi:10.48550/arxiv.2506.08929

Abstract

In this paper, we calibrate the luminosity relation of gamma-ray bursts (GRBs) by Artificial Neural Networks (ANN) which is employed to analyze the Pantheon+ sample of type Ia supernovae (SNe Ia) in a manner independent of cosmological assumptions. The A219 GRB dataset are used to calibrate the Amati relation (\(E_{\rm p}\)-\(E_{\rm iso}\)) at low redshift with the ANN framework, facilitating the construction of the Hubble diagram at higher redshifts. Cosmological models are constrained with GRBs at high-redshift and the latest observational Hubble data (OHD) via a Markov Chain Monte Carlo numerical approach. For the Chevallier-Polarski-Linder (CPL) model within a flat universe, we obtain \(Ω_{\rm m} = 0.321^+0.078_-0.069\), \(h = 0.654^+0.053_-0.071\), \(w₀ = -1.02^+0.67_-0.50\), and \(w_a = -0.98^+0.58_-0.58\) at the 1-\(σ\) confidence level, which indicating a preference for dark energy with potential redshift evolution (\(w_a ≠ 0\)). These findings by using ANN align closely with those derived from GRBs calibrated by using Gaussian Processes.

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• The Machine Learning to reconstruct GRB lightcurves