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arXiv · 2412.14750

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

Rahul Shah·Purba Mukherjee·Soumadeep Saha·Utpal Garain·Supratik Pal
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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 $\Lambda$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.

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Rahul Shah, Purba Mukherjee, Soumadeep Saha, Utpal Garain, Supratik Pal. 2024-12-19. Deep Learning Based Recalibration of SDSS and DESI BAO Alleviates Hubble and Clustering Tensions. https://arxiv.org/abs/2412.14750

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