Model-independent Gamma-Ray Bursts Constraints on Cosmological Models Using Machine Learning

Abstract

In this paper, we calibrate the luminosity relation of gamma-ray bursts (GRBs) with the machine learning (ML) algorithms from the Pantheon+ sample of type Ia supernovae in a cosmology-independent way. By using K-Nearest Neighbors (KNN) and Random Forest (RF) selected with the best performance in the ML algorithms, we calibrate the Amati relation (\unboldmath{$E_{\mathrm{p}}$-$E_{\mathrm{iso}}$}) relation with the A219 sample to construct the Hubble diagram of GRBs. Via the Markov Chain Monte Carlo numerical method with GRBs at high redshift and latest observational Hubble data, we find the results of constraints on cosmological models by using KNN and RF algorithms are consistent with those obtained from GRBs calibrated by using the Gaussian Process.

24 pages, 5 tables, 8 figures. Accepted for publication in Astrophysics and Space Science

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• The CosmoVerse White Paper: Addressing observational tensions in cosmology with systematics and fundamental physics
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• Gamma-Ray Bursts Calibrated from the Observational $H(z)$ Data in Artificial Neural Network Framework
• Testing the consistency of new Amati-correlated gamma-ray burst dataset cosmological constraints with those from better-established cosmological data
• Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample