Velázquez, J.D.J., Escamilla, L.A. orcid.org/0000-0003-4334-5140, Mukherjee, P. orcid.org/0000-0002-2701-5654 et al. (1 more author) (2024) Non-parametric reconstruction of cosmological observables using Gaussian processes regression. Universe, 10 (12). 464. ISSN 2218-1997
Abstract
The current accelerated expansion of the Universe remains one of the most intriguing topics in modern cosmology, driving the search for innovative statistical techniques. Recent advancements in machine learning have significantly enhanced its application across various scientific fields, including physics, and particularly cosmology, where data analysis plays a crucial role in problem-solving. In this work, a non-parametric regression method with Gaussian processes is presented along with several applications to reconstruct some cosmological observables, such as the deceleration parameter and the dark energy equation of state, in order to contribute some information that helps to clarify the behavior of the Universe. It was found that the results are consistent with λCDM and the predicted value of the Hubble parameter at redshift zero is H0 = 68.798 ± 6.340(1σ) km s−1 Mpc−1.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | cosmology; dark energy; Hubble parameter; deceleration parameter; linear regression; gaussian process |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 17 Jan 2025 15:43 |
Last Modified: | 17 Jan 2025 15:43 |
Status: | Published |
Publisher: | MDPI AG |
Refereed: | Yes |
Identification Number: | 10.3390/universe10120464 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:221711 |