Regular Article - Theoretical Physics
Measuring the Hubble constant with cosmic chronometers: a machine learning approach
Observatório Nacional, 20921-400, Rio de Janeiro, RJ, Brazil
2 Departamento de Física, Universidade do Estado do Rio Grande do Norte, 59610-210, Mossoró, RN, Brazil
3 Departamento de Física, Universidade Federal de Sergipe, 49000, São Cristóvão, SE, Brazil
Accepted: 19 June 2023
Published online: 29 June 2023
Local measurements of the Hubble constant () based on Cepheids e Type Ia supernova differ by from the estimated value of from Planck CMB observations under CDM assumptions. In order to better understand this tension, the comparison of different methods of analysis will be fundamental to interpret the data sets provided by the next generation of surveys. In this paper, we deploy machine learning algorithms to measure the through a regression analysis on synthetic data of the expansion rate assuming different values of redshift and different levels of uncertainty. We compare the performance of different regression algorithms as Extra-Trees, Artificial Neural Network, Gradient Boosting, Support Vector Machines, and we find that the Support Vector Machine exhibits the best performance in terms of bias-variance tradeoff in most cases, showing itself a competitive cross-check to non-supervised regression methods such as Gaussian Processes.
© The Author(s) 2023
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.