Regular Article - Theoretical Physics
Gaussian discriminators between CDM and wCDM cosmologies using expansion data
Department of Physics, Bu-Ali Sina University, 65178, 016016, Hamedan, Iran
2 Institute of Space Sciences and Astronomy, University of Malta, Msida, Malta
3 Department of Physics, University of Malta, Msida, Malta
Accepted: 22 August 2022
Published online: 9 September 2022
The Gaussian linear model provides a unique way to obtain the posterior probability distribution as well as the Bayesian evidence analytically. Considering the expansion rate data, the Gaussian linear model can be applied for CDM, wCDM and a non-flat CDM. In this paper, we simulate the expansion data with various precision and obtain the Bayesian evidence, then it has been used to discriminate the models. The data uncertainty is in range and two different sampling rates have been considered. Our results indicate that considering uncertainty, it is possible to discriminate 2 deviation in equation of state from . On the other hand, we investigate how precision of the expansion rate data affects discriminating the CDM from a non-flat CDM model. Finally, we perform a parameters inference in both the MCMC and Gaussian linear model, using current available expansion rate data and compare the results.
© The Author(s) 2022
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Funded by SCOAP3. SCOAP3 supports the goals of the International Year of Basic Sciences for Sustainable Development.