2024/12/02 by Domenico Sapone, Sapone, Domenico, Savvas Nesseris +1 · 1 voice · 1 citation
#astro-ph.CO #gr-qc #hep-ph
paper · pdf · doi:10.48550/arxiv.2412.01740
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 ΛCDM predictions with a best-fit value of w0 = -1.050 ± 0.128 and wa = 0.208 ± 0.546. These results raise the intriguing question of whether the identified outliers signal the presence of systematics or point towards new physics.