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DC Field | Value | Language |
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dc.contributor.author | Bernardo, Reginald Christian | - |
dc.contributor.author | Grandón, Daniela | - |
dc.contributor.author | Said, Jackson | - |
dc.contributor.author | Cárdenas, Víctor H. | - |
dc.date.accessioned | 2024-04-24T09:16:19Z | - |
dc.date.available | 2024-04-24T09:16:19Z | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | Bernardo, R. C., Grandón, D., Said, J. L., & Cárdenas, V. H. (2023). Dark energy by natural evolution: Constraining dark energy using Approximate Bayesian Computation. Physics of the Dark Universe, 40, 101213. | en_GB |
dc.identifier.issn | 22126864 | - |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/121229 | - |
dc.description.abstract | We look at dark energy from a biology inspired viewpoint by means of the Approximate Bayesian Computation (ABC) and late time cosmological observations. We find that dynamical dark energy comes out on top, or in the ABC language naturally selected, over the standard CDM cosmological scenario. We confirm this conclusion is robust to whether baryon acoustic oscillations and Hubble constant priors are considered. Our results show that the algorithm prefers low values of the Hubble constant, consistent or at least a few standard deviation away from the cosmic microwave background estimate, regardless of the priors taken initially in each model. This supports the result of the traditional MCMC analysis and could be viewed as strengthening evidence for dynamical dark energy being a more favorable model of late time cosmology. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Elsevier BV | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Bayesian statistical decision theory | en_GB |
dc.subject | Gravitation | en_GB |
dc.subject | Gravity | en_GB |
dc.subject | Dark energy (Astronomy) | en_GB |
dc.subject | Cosmology -- Mathematical models | en_GB |
dc.subject | Statistical physics | en_GB |
dc.title | Dark energy by natural evolution : constraining dark energy using approximate bayesian computation | en_GB |
dc.type | article | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.1016/j.dark.2023.101213 | - |
dc.publication.title | Physics of the Dark Universe | en_GB |
Appears in Collections: | Scholarly Works - InsSSA |
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