Deep Learning Based Recalibration of SDSS and DESI BAO Alleviates Hubble and Clustering Tensions

TL;DR

A recalibration of two independent BAO datasets, SDSS and DESI, is presented by employing deep learning techniques for model-independent estimation of r_d, and the impacts on $\Lambda$CDM cosmological parameters are explored.

Abstract

Conventional calibration of Baryon Acoustic Oscillations (BAO) data relies on estimation of the sound horizon at drag epoch $r_d$ from early universe observations by assuming a cosmological model. We present a recalibration of two independent BAO datasets, SDSS and DESI, by employing deep learning techniques for model-independent estimation of $r_d$, and explore the impacts on $Λ$CDM cosmological parameters. Significant reductions in both Hubble ($H_0$) and clustering ($S_8$) tensions are observed for both the recalibrated datasets. Moderate shifts in some other parameters hint towards further exploration of such data-driven approaches.

Figures (2)

• Figure 1: Comparison of $H_0$-$S_8$ constraints for different combinations of calibration and datasets. The gray and red shaded regions correspond to the late-time measurements of $H_0=73.04\pm1.04 \text{ km s}^{-1}\text{Mpc}^{-1}$Riess:2021jrx and $S_8 = 0.759\pm0.024$DES:2021bvc.
• Figure 2: Comparison of constraints for different combinations of calibration and datasets. $H_0$ is in units of $\text{km s}^{-1} \text{ Mpc}^{-1}$.