A sound-horizon-free measurement of the Hubble constant from DESI DR2 baryon acoustic oscillations using artificial neural networks

TL;DR

This paper introduces a model-independent, sound-horizon-free method to measure the Hubble constant using BAO data and neural networks, avoiding standard cosmological priors.

Contribution

It presents a novel neural network-based approach that combines multiple observational probes to estimate H_0 without relying on sound horizon assumptions.

Findings

Measured H_0 = 71.5 ± 2.2 km/s/Mpc, consistent with local and some other measurements.

Supports the existence of the Hubble tension by favoring a higher H_0 than Planck CMB results.

Uses a data-driven neural network method with 4096 neurons and bootstrap analysis.

Abstract

We present a model-independent, sound-horizon-free measurement of the Hubble constant $H_0$ using baryon acoustic oscillation tracers from the Dark Energy Spectroscopic Instrument Data Release 2. The function reconstructions are performed using the artificial neural network method, which is a completely data-driven approach that avoids the mild $\Lambda$CDM prior dependence. Our approach is based on the distance duality relation and combines three complementary observational probes, such as Type Ia supernovae, cosmic chronometer, and DESI DR2 BAO -- without requiring any knowledge of the sound horizon scale $r_d$ or any assumption about the absolute luminosity of SNe Ia. We obtain a joint constraint of $H_0 = 71.5\pm2.2$ km s$^{-1}$ Mpc$^{-1}$ at 68\% confidence for 1000 bootstrap realisations and 4096 neurons, which is consistent with the TRGB result and the SH0ES measurement within $0.6\sigma$, consistent with the Planck 2020 result within $2\sigma$. Our results favor a higher value of $H_0$ compared to the Planck CMB inference, adding independent support for the reality of the Hubble tension.