Outliers in DESI BAO: robustness and cosmological implications

TL;DR

This paper uses Bayesian analysis to identify outliers in DESI BAO data, improving dataset robustness and confirming consistency with ΛCDM predictions, while raising questions about potential systematics or new physics.

Contribution

It introduces an Internal Robustness analysis to detect outliers in DESI BAO data, enhancing data reliability and cosmological inference.

Findings

Identified three potential outliers at specific redshifts.

Excluding outliers improves dataset robustness.

Results are consistent with ΛCDM predictions.

Abstract

We apply an Internal Robustness (iR) analysis to the recently released Dark Energy Spectroscopic Instrument (DESI) baryon acoustic oscillations dataset. This approach examines combinations of data subsets through a fully Bayesian model comparison, aiming to identify potential outliers, subsets possibly influenced by systematic errors, or hints of new physics. Using this approach, we identify three data points at $z= 0.295,\,0.51,\,0.71$ as potential outliers. Excluding these points improves the internal robustness of the dataset by minimizing statistical anomalies and enables the recovery of $\Lambda$CDM predictions with a best-fit value of $w_0 = -1.050 \pm 0.128$ and $w_a = 0.208 \pm 0.546$. These results raise the intriguing question of whether the identified outliers signal the presence of systematics or point towards new physics.