Cosmological constraints from a joint DESI DR1 Full-Shape and DR2 BAO

TL;DR

This paper combines DESI DR1 full-shape clustering data with DR2 BAO measurements using a novel compression method, enabling robust Bayesian constraints on cosmological models including extensions beyond $ ext{Lambda}$CDM.

Contribution

It introduces the ShapeFit compression technique for joint analysis of DESI data, providing the first reliable DESI-only Bayesian constraints on extended cosmological models.

Findings

Precise constraints on $ ext{Omega}_m$, $h$, and $ ext{sigma}_8$ within flat $ ext{Lambda}$CDM.

Improved constraints on dark energy parameters $w_0$ and $w_a$, reducing discrepancy with $ ext{Lambda}$CDM.

Competitive limits on neutrino masses and spatial curvature.

Abstract

We present a cosmological analysis combining full-shape (FS) clustering measurements from the Dark Energy Spectroscopic Instrument (DESI) DR1 with baryon acoustic oscillation (BAO) measurements from DESI DR2. To achieve a robust combination that accounts for the correlation between the two data releases, we employ the ShapeFit compression method and estimate the joint covariance using EZmocks. This compressed approach inherently mitigates the prior volume effects that have previously dominated Bayesian constraints from DESI data with minimal external priors. Consequently, we obtain--for the first time within a Bayesian framework--reliable DESI-only constraints on extensions to $\Lambda$CDM using only a Big Bang Nucleosynthesis prior on the baryon density and a wide prior on the spectral index. In flat $\Lambda$CDM, we find $\Omega_m = 0.3035 \pm 0.0085$, $h = 0.6876 \pm 0.0059$, and $\sigma_8 = 0.822 \pm 0.034$. For the $w_0 w_a$CDM dynamical dark energy model, we measure $w_0 = 0.49 \pm 0.25$ and $w_a = -1.52 \pm 0.77$, improving constraints by $\sim 30\%$ relative to the analogous DR1 measurement and reducing the discrepancy with $\Lambda$CDM to $1.4\sigma$ when compared to BAO only analyses. We also report competitive limits on the sum of neutrino masses and spatial curvature. This work demonstrates that the ShapeFit compression provides a prior-robust and computationally efficient pathway to constrain beyond-$\Lambda$CDM physics with large-scale structure.