Measuring the Hubble Constant with cosmic chronometers: a machine learning approach

TL;DR

This paper employs machine learning regression algorithms to measure the Hubble constant from synthetic cosmic chronometer data, aiming to address the existing tension between local and CMB-based measurements.

Contribution

It compares various machine learning algorithms for estimating $H_0$, identifying Support Vector Machines as the most effective method for this purpose.

Findings

Support Vector Machine outperforms other algorithms in bias-variance tradeoff.

Machine learning methods provide a competitive cross-check to Gaussian Processes.

The approach offers a new way to interpret cosmic chronometer data for $H_0$ estimation.

Abstract

Local measurements of the Hubble constant ($H_0$) based on Cepheids e Type Ia supernova differ by $\approx 5 \sigma$ from the estimated value of $H_0$ from Planck CMB observations under $\Lambda$CDM assumptions. In order to better understand this $H_0$ 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 $H_0$ 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.