Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/127692
Title: Estimation of the Hubble constant using Gaussian process regression and viable alternatives
Authors: Zammit, Samuel
Suda, David
Sammut, Fiona
Said, Jackson
Keywords: Cosmology
Gaussian processes
Cosmological constants
Expanding universe
Issue Date: 2024
Publisher: Springer Berlin Heidelberg
Citation: Zammit, S., Suda, D., Sammut, F. and Said, J.L. (2024). Estimation of the Hubble Constant Using Gaussian Process Regression and Viable Alternatives. The European Physical Journal C, 84(9), 987.
Abstract: A well-known problem in cosmology is the ‘Hubble tension’ problem, i.e. different estimates for the Hubble constant H0 are not concordant with each other. This work investigates different statistical methods for estimating this value using cosmic chronometer, type Ia supernova and baryonic acoustic data. We start by making use of methods already established in the literature for this purpose, namely Gaussian process regression and Markov chain Monte Carlo (MCMC) methods based on the concordance Lambda CDM model. We also consider two novel approaches; the first makes use of non-parametric MCMC inference on the hyperparameters of a Gaussian process kernel, independently of any cosmological model. The second approach is Student’s t-process regression, which is a generalised version of Gaussian process regression that makes use of the multivariate Student’s t-distribution instead of the multivariate Gaussian distribution. We also consider variants of these two methods which account for heteroscedasticity within the data. A comparison of the different approaches is made. In particular, the model-independent techniques investigated mostly agree with predictions based on the Lambda CDM model. Moreover, Gaussian process regression is highly sensitive to the prior specification, while Student’s t-process regression and the heteroscedastic variants of both methods are more robust to this. Student’s t-process regression and both heteroscedastic models suggest a lower value of the Hubble constant. Across all the estimates obtained for the Hubble constant within this work, the median value is 68.85 +/- 1.67 km s^-1 Mpc^-1.
URI: https://www.um.edu.mt/library/oar/handle/123456789/127692
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