arXiv · 2603.05472
The Bayesian view of DESI DR2 with unimpeded: Evidence and tension in a combined analysis with CMB and supernovae across cosmological models
Abstract
We apply the $\texttt{unimpeded}$ framework to perform a fully Bayesian reanalysis of the DESI DR2 data, using nested sampling with $\texttt{PolyChord}$ to compute evidences for $\Lambda$CDM and seven extensions across combinations of DESI DR1/DR2, Planck CMB, supernovae (Pantheon+, Union3, DES-SN5YR, DES-Dovekie), and DES-Y1 weak lensing. The Bayesian Ockham's razor penalises extended models, yielding weaker or opposite preferences compared to $\Delta\chi^2$-based analyses. For DESI DR2 BAO combined with Planck CMB alone, the DESI collaboration's $3.1\sigma$ frequentist preference for $w_0w_a$CDM is eliminated entirely: we obtain ${\ln B = -0.57{\scriptstyle\pm0.26}}$, modestly favouring $\Lambda$CDM. Adding DES-Dovekie, the recalibration of DES-SN5YR, maintains this concordance (${\ln B = -0.30{\scriptstyle\pm0.19}}$). However, when the earlier DES-SN5YR calibration is included instead, the DESI collaboration's $4.2\sigma$ result survives the Bayesian Ockham penalty as a $3.07{\scriptstyle\pm0.10},\sigma$ preference (${\ln B = +3.32{\scriptstyle\pm0.27}}$). That this signal persists despite the Ockham penalty makes the role of tension quantification essential: our analysis traced the preference to the earlier DES-SN5YR calibration, which introduced a $2.95{\scriptstyle\pm 0.04},\sigma$ conflict with DESI DR2 within $\Lambda$CDM -- a tension that stands out from the grid -- reduced to $1.96{\scriptstyle\pm 0.04},\sigma$ with the DES-Dovekie recalibration. With DES-Dovekie, the Bayesian evidence for dynamical dark energy vanishes.
Explore related subjects
Keep this discovery
Dily Duan Yi Ong, David Yallup, Will Handley. 2026-03-05. The Bayesian view of DESI DR2 with unimpeded: Evidence and tension in a combined analysis with CMB and supernovae across cosmological models. https://arxiv.org/abs/2603.05472
Cite the original work for its findings. Save a collection to share your selection of sources.