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https://www.um.edu.mt/library/oar/handle/123456789/89407
Title: | Towards a model-independent reconstruction approach for late-time Hubble data |
Authors: | Bernardo, Reginald Christian Said, Jackson |
Keywords: | Dark energy (Astronomy) Gaussian processes Kernel functions Bayesian field theory Monte Carlo method |
Issue Date: | 2021 |
Publisher: | Institute of Physics Publishing Ltd. |
Citation: | Bernardo, R. C., & Said, J. L. (2021). Towards a model-independent reconstruction approach for late-time Hubble data. Journal of Cosmology and Astroparticle Physics, 2021(08), 027. |
Abstract: | Gaussian processes offers a convenient way to perform nonparametric reconstructions of observational data assuming only a kernel which describes the covariance between neighbouring points in a data set. We approach the ambiguity in the choice of kernel in Gaussian processes with two methods - (a) approximate Bayesian computation with sequential Monte Carlo sampling and (b) genetic algorithm - and use the overall resulting method to reconstruct the cosmic chronometers and supernovae type Ia data sets. The results have shown that the Matérn( ν = 5/2 ) kernel emerges on top of the two-hyperparameter family of kernels for both cosmological data sets. On the other hand, we use the genetic algorithm in order to select a most naturally-fit kernel among a competitive pool made up of a ten-hyperparameters class of kernels. Imposing a Bayesian information criterion-inspired measure of the fitness, the results have shown that a hybrid of the Radial Basis Function and the Matérn( ν = 5/2 ) kernel best represented both data sets. The kernel selection problem is not totally closed and may benefit from further analysis using other strategies to resolve an optimal kernel for a particular data set. |
URI: | https://www.um.edu.mt/library/oar/handle/123456789/89407 |
Appears in Collections: | Scholarly Works - InsSSA |
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File | Description | Size | Format | |
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Towards_a_model_independent_reconstruction_approach_for_late_time_Hubble_data_2021.pdf Restricted Access | 2.19 MB | Adobe PDF | View/Open Request a copy |
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