Reanalyzing DESI DR1: 4. Percent-Level Cosmological Constraints from Combined Probes and Robust Evidence for the Normal Neutrino Mass Hierarchy

Abstract

We present cosmological parameters measurements from the full combination of DESI DR1 galaxy clustering data described with large-scale structure effective field theory. By incorporating additional datasets (photometric galaxies and CMB lensing cross-correlations) and extending the bispectrum likelihood to smaller scales using a consistent one-loop theory computation, we achieve substantial gains in constraining power relative to previous analyses. Combining with the latest DESI baryon acoustic oscillation data and using cosmic microwave background (CMB) priors on the power spectrum tilt and baryon density, we obtain tight constraints on the $\Lambda$CDM model, finding the Hubble constant $H_0=69.08\pm 0.37~\mathrm{km}\,\mathrm{s}^{-1}\mathrm{Mpc}^{-1}$, the matter density fraction $\Omega_m=0.2973\pm 0.0050$, and the mass fluctuation amplitude $\sigma_8 = 0.815\pm 0.016$ (or the lensing parameter $S_8\equiv\sigma_8\sqrt{\Omega_m/0.3}=0.811\pm 0.016$), corresponding to $0.6\%$, $1.7\%$, and $2\%$ precision respectively. Adding the Pantheon+ supernova sample (SNe), we find a preference of $2.6\sigma$ for the $w_0w_a$ dynamical dark energy model from low-redshift data alone, which increases to $2.8\sigma$ when exchanging the SNe with Planck CMB data. Combining full-shape data with BAO, CMB, and SNe likelihoods, we improve the dark energy figure-of-merit by $18\%$ and bound the sum of the neutrino masses to $M_\nu<0.057$ eV in $\Lambda$CDM and $M_\nu<0.095$ eV in the $w_0w_a$ dynamical dark energy model (both at 95\% CL). This represents an improvement of $25\%$ over the background expansion constraints and the strongest bound on neutrino masses in $w_0w_a$CDM to date. Our results suggest that the preference for the normal ordering of neutrino mass states holds regardless of the cosmological background model, and is robust in light of tensions between cosmological datasets.