Dark Energy Survey Year 3 results: $w$CDM cosmology from simulation-based inference with persistent homology on the sphere
J. Prat, M. Gatti, C. Doux, P. Pranav, C. Chang, N. Jeffrey, L. Whiteway, D. Anbajagane, S. Sugiyama, A. Thomsen, A. Alarcon, A. Amon, K. Bechtol, G. M. Bernstein, A. Campos, R. Chen, A. Choi, C. Davis, J. DeRose, S. Dodelson, K. Eckert, J. Elvin-Poole, S. Everett, A. Fert\'e

TL;DR
This paper introduces a novel application of spherical persistent homology to DES Y3 weak lensing data, providing tighter cosmological constraints and demonstrating the robustness of topological methods in cosmology.
Contribution
It develops a spherical persistent homology approach optimized for curved-sky galaxy surveys and integrates it with simulation-based inference for cosmological parameter estimation.
Findings
Achieved 70% tighter constraints on $S_8$ and $ m ext{O}m$ compared to traditional methods.
Validated minimal bias ($<0.3\sigma$) from baryonic feedback effects.
Demonstrated the effectiveness of topological summaries in extracting cosmological information.
Abstract
We present cosmological constraints from Dark Energy Survey Year 3 (DES Y3) weak lensing data using persistent homology, a topological data analysis technique that tracks how features like clusters and voids evolve across density thresholds. For the first time, we apply spherical persistent homology to galaxy survey data through the algorithm TopoS2, which is optimized for curved-sky analyses and HEALPix compatibility. Employing a simulation-based inference framework with the Gower Street simulation suite, specifically designed to mimic DES Y3 data properties, we extract topological summary statistics from convergence maps across multiple smoothing scales and redshift bins. After neural network compression of these statistics, we estimate the likelihood function and validate our analysis against baryonic feedback effects, finding minimal biases (under ) in the…
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Taxonomy
TopicsTopological and Geometric Data Analysis
