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

• Impact Factor:2.6
• DOI number:10.3390/universe11080241
• Teaching and Research Group:天体物理
• Journal:Universe
• Key Words:gamma-ray bursts;general cosmology;dark energy cosmology;observations
• Abstract:In this paper,we calibrate the luminosity relation of gamma-ray bursts (GRBs) by employing artificial neural networks (ANNs) to analyze the Pantheon+ sample of type Ia supernovae (SNe Ia) in a manner independent of cosmological assumptions. The A219 GRB dataset is used to calibrate the Amati relation (Ep–Eiso) 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 the Markov chain Monte Carlo numerical approach. For the Chevallier–Polarski–Linder (CPL) model within a flat universe,we obtain Ωm = 0.321+0.078−0.069,h = 0.654+0.053−0.071,w0 = −1.02+0.67−0.50,and wa = −0.98+0.58−0.58 at the 1σ confidence level,which indicates a preference for dark energy with potential redshift evolution (wa ≠ 0). These findings using ANNs align closely with those derived from GRBs calibrated using Gaussian processes (GPs).
• Co-author:Luo X,Zhang B,Feng JC,Wu PX,Liu Y,Liang N
• First Author:Huan Z
• Document Code:010
• Discipline:Science
• First-Level Discipline:Astronomy
• Volume:11
• Page Number:241
• ISSN No.:2218-1997
• Translation or Not:no
• Date of Publication:2025-07-23