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

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 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.

Tasks

Reproductions