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 ( ) 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 cold dark matter ( ) parameters (including the sum of neutrino masses) and 52 astrophysical modeling parameters, we measure and . Compared to constraints from primary cosmic microwave background (CMB) anisotropies, our constraints are only 15% wider with a probability to exceed of 0.22 ( ) for the two-parameter difference. We further obtain which is lower than the measurement at the level. The combined SPT cluster, DES , and datasets mildly prefer a nonzero positive neutrino mass, with a 95% upper limit on the sum of neutrino masses. Assuming a model, we constrain the dark energy equation of state parameter and when combining with primary CMB anisotropies, we recover , a 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. Published by the American Physical Society2025
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This content will become publicly available on December 2, 2026
IAEmu: Learning Galaxy Intrinsic Alignment Correlations
The intrinsic alignments (IA) of galaxies, a key contaminant in weak lensing analyses, arise from correlations in galaxy shapes driven by tidal interactions and galaxy formation processes. Accurate IA modeling is essential for robust cosmological inference, but current approaches rely on perturbative methods that break down on nonlinear scales or on expensive simulations. We introduce IAEmu, a neural network-based emulator that predicts the galaxy position-position ( ), position-orientation ( ), and orientation-orientation ( ) correlation functions and their uncertainties using mock catalogs based on the halo occupation distribution (HOD) framework. Compared to simulations, IAEmu achieves ~3% average error for and ~5% for , while capturing the stochasticity of without overfitting. The emulator provides both aleatoric and epistemic uncertainties, helping identify regions where predictions may be less reliable. We also demonstrate generalization to non-HOD alignment signals by fitting to IllustrisTNG hydrodynamical simulation data. As a fully differentiable neural network, IAEmu enables $ $ speed-ups in mapping HOD parameters to correlation functions on GPUs, compared to CPU-based simulations. This acceleration facilitates inverse modeling via gradient-based sampling, making IAEmu a powerful surrogate model for galaxy bias and IA studies with direct applications to Stage IV weak lensing surveys.
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- PAR ID:
- 10654246
- Publisher / Repository:
- arXiv
- Date Published:
- Journal Name:
- The Open Journal of Astrophysics
- Volume:
- 8
- ISSN:
- 2565-6120
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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