Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/89924
Title: Observational constraints on beyond general relativity models
Authors: Pullicino, Nathan Lee (2021)
Keywords: Gravitation
General relativity (Physics)
Cosmology
Markov processes
Issue Date: 2021
Citation: Pullicino, N. L. (2021). Observational constraints on beyond general relativity models (Master’s dissertation).
Abstract: This study consists of a detailed analysis of the impact of Hubble constant prior values from recent surveys on the constraints of cosmological parameters of five models in f(T) cosmology. The data used to constrain these models involves cosmic chronometers, the Pantheon supernovae type Ia data set and baryon acoustic oscillation data. The cosmological parameters which are allowed to vary in the Markov chain Monte Carlo analysis are the Hubble constant H0, the matter density parameter at current time Ωm,0 and the model parameter bi where (i = 1,2,3,4,5) depending on the model being considered. The H0 priors considered in this study are the Riess 2019, the tip of the red giant branch and the H0LiCOW priors as they have contributed to the recent H0 tension problem with the ΛCDM model. These priors impact all the cosmological parameters considered in this study. The introduction of a prior increases the posterior value of H0 depending on how large the prior value is. The uncertainty of this parameter is also lowered. Furthermore, Ωm,0 is affected in different ways depending on whether baryon acoustic oscillation data is included in the analysis. The introduction of this data results in lower posterior values of H0. ∆AIC and ∆BIC indicate that most of the considered models are quite close to ΛCDM. In models which have a ΛCDM limit, the obtained parameter values indicate that ΛCDM is preferred. Furthermore, in most cases the H0 tension is alleviated while in some, it is even resolved.
Description: M.Sc.(Melit.)
URI: https://www.um.edu.mt/library/oar/handle/123456789/89924
Appears in Collections:Dissertations - InsSSA - 2021

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