https://doi.org/10.1140/epjc/s10052-023-11734-1
Regular Article - Theoretical Physics
Measuring the Hubble constant with cosmic chronometers: a machine learning approach
1
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
Received:
24
October
2022
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
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