Tightening Cosmological Constraints Within and Beyond $\Lambda$CDM Using Gamma-Ray Bursts Calibrated with Type Ia Supernovae
Wei Hong, Luca Izzo, Massimo Della Valle, Orlando Luongo, Marco Muccino, Tong-Jie Zhang

TL;DR
This paper develops a model-independent method to calibrate gamma-ray bursts as high-redshift distance indicators, combining neural network reconstructions with supernova data to improve cosmological constraints beyond the standard model.
Contribution
It introduces a novel, self-consistent calibration technique for GRBs using neural networks and supernova data, extending the cosmic distance ladder to higher redshifts.
Findings
Consistent cosmological constraints from two GRB correlations.
Hubble constant aligns with supernova measurements.
High-redshift GRBs suggest higher matter density and potential dark energy evolution.
Abstract
Context. Gamma-ray bursts (GRBs) reach redshifts beyond Type Ia supernovae (SNe Ia) and can extend distance measurements into the early Universe, but their use as distance indicators is limited by the circularity problem in calibrating empirical luminosity relations. Aims. We present a model-independent methodology to overcome this circularity by combining Pantheon SNe Ia, a distance reconstruction based on artificial neural networks (ANNs), and two GRB correlations (Amati and Combo) into a distance ladder from low to high redshift, with the goal of constraining cosmological parameters in and . Methods. We use the ReFANN to reconstruct the luminosity distance and distance modulus from the Pantheon dataset, with hyperparameters optimized via approximate Bayesian computation rejection and a risk function. This…
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Taxonomy
TopicsGamma-ray bursts and supernovae · Statistical Mechanics and Entropy · Astronomy and Astrophysical Research
