Sound-Horizon-Agnostic Inference of the Hubble Constant and Neutrino Mass from BAO, CMB Lensing, and Galaxy Weak Lensing and Clustering (2509.16202v1)
Abstract: We present a sound-horizon-agnostic determination of the Hubble constant, $H_0$, by combining DESI DR2 baryon acoustic oscillation (BAO) data with the latest cosmic microwave background (CMB) lensing measurements from Planck, ACT, and SPT-3G, the angular size of the CMB acoustic scale, Dark Energy Survey Year-3 ($3\times2$-pt) galaxy weak lensing and clustering correlations, and the Pantheon+ supernova sample. In this analysis, the sound horizon at the drag epoch, $r_d$, is treated as a free parameter, avoiding assumptions about early-Universe physics. By combining uncalibrated comoving distances from BAO and supernovae with constraints on the matter density $\Omega_m h2$ from CMB and galaxy lensing/clustering, we break the $r_d$-$H_0$ degeneracy and obtain $H_0 = 70.0 \pm 1.7$ km/s/Mpc when the sum of the neutrino masses is fixed at $\Sigma m_\nu = 0.06$ eV. With a conservative prior on the amplitude of primordial fluctuations, $A_s$, we find $H_0 = 70.03 \pm 0.97$ km/s/Mpc and $r_d = 144.8 \pm 1.6$ Mpc. Allowing $\Sigma m_\nu$ to vary yields $H_0 = 75.3{+3.3}_{-4.0}$ km/s/Mpc and $\Sigma m_\nu = 0.55{+0.23}_{-0.37}$ ($<1.11$ eV) at 68% (95%) CL, and $H_0 = 73.9 \pm 2.2$ km/s/Mpc with $\Sigma m_\nu = 0.46{+0.21}_{-0.25}$ ($=0.46{+0.40}_{-0.45}$ eV) at 68% (95%) CL when a prior on $A_s$ is applied. Forecasts for the completed DESI BAO program, combined with Simons-Observatory-like CMB lensing, next-generation $3\times2$-pt data, and expanded supernova samples predict $\sigma(H_0) \simeq 0.67$ km/s/Mpc with fixed $\Sigma m_\nu$, and $\sigma(H_0) \simeq 1.1$ km/s/Mpc with $\Sigma m_\nu < 0.133$ ($<0.263$) eV at 68% (95%) CL when the neutrino mass is varied. As the precision of BAO, CMB lensing, and galaxy lensing/clustering improve, this $r_d$-agnostic framework will provide an independent test of the need for new physics at recombination.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.