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

Rahul Shah, Purba Mukherjee, Soumadeep Saha, Utpal Garain, Supratik Pal
12/19/2024
astro-ph.COastro-ph.IMcs.LG

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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 $Λ$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.

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Cite this paper

@article{shah2024deep,
  title  = {Deep Learning Based Recalibration of SDSS and DESI BAO Alleviates Hubble and Clustering Tensions},
  author = {Rahul Shah and Purba Mukherjee and Soumadeep Saha and Utpal Garain and Supratik Pal},
  year   = {2024},
  eprint = {2412.14750},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2412.14750v1}
}

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