Multiprobe cosmology from the abundance of SPT clusters and des galaxy clustering and weak lensing - Bocquet, S.; Grandis, S.; Krause, E.; To, C.; Bleem, L. E.; Klein, M.; Mohr, J. J.; Schrabback, T.; Alarcon, A.; Alves, O.; Amon, A.; Andrade-Oliveira, F.; et al. (American Physical Society (APS), 2025)
Cosmic shear, galaxy clustering, and the abundance of massive halos each probe the large-scale structure of the Universe in complementary ways. We present cosmological constraints from the joint analysis of the three probes, building on the latest analyses of the lensing-informed abundance of clusters identified by the South Pole Telescope (SPT) and of the auto- and cross-correlation of galaxy position and weak lensing measurements (3×2pt) in the Dark Energy Survey (DES). We consider the cosmological correlation between the different tracers and we account for the systematic uncertainties that are shared between the large-scale lensing correlation functions and the small-scale lensing-based cluster mass calibration. Marginalized over the remaining Λ cold dark matter (ΛCDM) parameters (including the sum of neutrino masses) and 52 astrophysical modeling parameters, we measure ωm=0.300±0.017 and σ8=0.797±0.026. Compared to constraints from Planck primary cosmic microwave background (CMB) anisotropies, our constraints are only 15% wider with a probability to exceed of 0.22 (1.2σ) for the two-parameter difference. We further obtain S8σ8(ωm/0.3)0.5=0.796±0.013 which is lower than the Planck measurement at the 1.6σ level. The combined SPT cluster, DES 3×2pt, and Planck datasets mildly prefer a nonzero positive neutrino mass, with a 95% upper limit mν<0.25 eV on the sum of neutrino masses. Assuming a wCDM model, we constrain the dark energy equation of state parameter w=-1.15-0.17+0.23 and when combining with Planck primary CMB anisotropies, we recover w=-1.20-0.09+0.15, a 1.7σ difference with a cosmological constant. The precision of our results highlights the benefits of multiwavelength multiprobe cosmology and our analysis paves the way for upcoming joint analyses of next-generation datasets.