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

arXiv: https://arxiv.org/abs/2412.14750 · HTML: https://mlsanity.com/paper/2412.14750/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 32 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Builds on (most cited first, 1 of 1)

- [LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring its Applications](https://mlsanity.com/paper/2401.17029/) (2024)

## Built on by (most cited first, 1 of 1)

- [A New ~ 5σ Tension at Characteristic Redshift from DESI-DR1 BAO and DES-SN5YR Observations](https://mlsanity.com/paper/2503.02880/) (2025)

## Abstract (arXiv)

Conventional calibration of Baryon Acoustic Oscillations (BAO) data relies on estimation of the sound horizon at drag epoch rd 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 rd, and explore the impacts on LambdaCDM cosmological parameters. Significant reductions in both Hubble (H0) and clustering (S8) tensions are observed for both the recalibrated datasets. Moderate shifts in some other parameters hint towards further exploration of such data-driven approaches.