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

Rahul Shah; Purba Mukherjee; Soumadeep Saha; Utpal Garain; Supratik Pal

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

Cosmology and Nongalactic Astrophysics 2024-12-20 v1 Instrumentation and Methods for Astrophysics Machine Learning

Authors: Rahul Shah , Purba Mukherjee , Soumadeep Saha , Utpal Garain , Supratik Pal

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.

Keywords

baryon acoustic oscillations sound horizon deep learning Hubble tension clustering tension SDSS DESI ΛCDM

Cite

@article{arxiv.2412.14750,
  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},
  journal= {arXiv preprint arXiv:2412.14750},
  year   = {2024}
}

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5 pages, 2 figures, 2 tables. Comments are welcome

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

Abstract

Keywords

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