Machine Learning Cosmic Expansion History

2017/12/26 by Deng Wang, Wei Zhang, Wang, Deng +1
Computer Science · Physics and Astronomy · #Astronomy and Astrophysical Research #Computational Physics and Python Applications #Cosmology and Gravitation Theories #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #FOS: Physical sciences #General Relativity and Quantum Cosmology (gr-qc) #High Energy Astrophysical Phenomena (astro-ph.HE)

paper · pdf · doi:10.48550/arxiv.1712.09208

openalex publication_date 2017/12/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We use the machine learning techniques, for the first time, to study the background evolution of the universe in light of 30 cosmic chronometers. From 7 machine learning algorithms, using the principle of mean squared error minimization on testing set, we find that Bayesian ridge regression is the optimal method to extract the information from cosmic chronometers. By use of a power-law polynomial expansion, we obtain the first Hubble constant estimation H₀=65.95^+6.98_-6.36 km s^-1 Mpc^-1 from machine learning. From the view of machine learning, we may rule out a large number of cosmological models, the number of physical parameters of which containing H₀ is larger than 3. Very importantly and interestingly, we find that the parameter spaces of 3 specific cosmological models can all be clearly compressed by considering both their explanation and generalization abilities.

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