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

Abstract

Local measurements of the Hubble constant ($$H_0$$ H 0 ) based on Cepheids e Type Ia supernova differ by $$\approx 5 \sigma $$ ≈ 5 σ from the estimated value of $$H_0$$ H 0 from Planck CMB observations under $$\Lambda $$ Λ CDM assumptions. In order to better understand this $$H_0$$ 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 $$H_0$$ 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.

Deep Learning Voigt Profiles. I. Single-Cloud Doublets

“…Then, real-world data are presented to the network, which predicts the characteristic properties in the real data. Successful applications include measuring galactic star formation rates (e.g., Delli Veneri et al 2019;Simet et al 2021;Euclid Collaboration et al 2023;Santos-Olmsted et al 2023), metallicities (e.g., Liew-Cain et al 2021), stellar masses and redshifts (e.g., Bonjean et al 2019;Wu & Boada 2019;Surana et al 2020), masses of galaxy clusters (e.g., Ntampaka et al 2015), cosmological parameters from weak lensing (e.g., Gupta et al 2018), and large-scale structure formation (e.g., He et al 2018), identifying reionization sources (e.g., Hassan et al 2019) and the duration of reionization (e.g., La Plante & Ntampaka 2019), and constraining cosmological parameters (e.g., Fluri et al 2019;Ribli et al 2019;Hassan et al 2020;Matilla et al 2020;Ntampaka et al 2020;Ntampaka & Vikhlinin 2022;Andrianomena & Hassan 2023;Bengaly et al 2023;Lu et al 2023;Novaes et al 2024;Qiu et al 2023). Recently, Monadi et al (2023) applied Gaussian processes to Sloan Digital Sky Survey (SDSS) DR12 quasar spectra to detect C IV absorbers and measure their VP parameters.…”