Escamilla, L.A., Akarsu, Ö, Di Valentino, E. orcid.org/0000-0001-8408-6961 et al. (1 more author) (2023) Model-independent reconstruction of the interacting dark energy kernel: Binned and Gaussian process. Journal of Cosmology and Astroparticle Physics, 2023 (11). 051. ISSN 1475-7516
Abstract
The cosmological dark sector remains an enigma, offering numerous possibilities for exploration. One particularly intriguing option is the (non-minimal) interaction scenario between dark matter and dark energy. In this paper, to investigate this scenario, we have implemented Binned and Gaussian model-independent reconstructions for the interaction kernel alongside the equation of state; while using data from BAOs, Pantheon+ and Cosmic Chronometers. In addition to the reconstruction process, we conducted a model selection to analyze how our methodology performed against the standard ΛCDM model. The results revealed a slight indication, of at least 1σ confidence level, for some oscillatory dynamics in the interaction kernel and, as a by-product, also in the DE and DM. A consequence of this outcome is the possibility of a sign change in the direction of the energy transfer between DE and DM and a possible transition from a negative DE energy density in early-times to a positive one at late-times. While our reconstructions provided a better fit to the data compared to the standard model, the Bayesian Evidence showed an intrinsic penalization due to the extra degrees of freedom. Nevertheless these reconstructions could be used as a basis for other physical models with lower complexity but similar behavior.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Author(s). Published by IOP Publishing Ltd on behalf of Sissa Medialab. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence (http://creativecommons.org/licenses/by/4.0/). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
Keywords: | Bayesian reasoning; dark energy theory; Machine learning |
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: | 23 Nov 2023 15:31 |
Last Modified: | 23 Nov 2023 15:31 |
Status: | Published |
Publisher: | IOP Publishing |
Refereed: | Yes |
Identification Number: | 10.1088/1475-7516/2023/11/051 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:205528 |
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