Deep Learning Based Recalibration of SDSS and DESI BAO Alleviates Hubble and Clustering Tensions
This addresses tensions in cosmological parameters for astrophysics, offering a data-driven approach that is incremental in applying deep learning to an existing calibration bottleneck.
The authors tackled the problem of model-dependent calibration in Baryon Acoustic Oscillations (BAO) data by using deep learning for a model-independent estimation of the sound horizon, resulting in significant reductions in Hubble and clustering tensions for SDSS and DESI datasets.
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.