Dark Energy Survey Year 3 results: <i>w</i> CDM cosmology from simulation-based inference with persistent homology on the sphere

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 $0.3\sigma$) in the $\Omega _\mathrm{m}-S_8$ plane. Assuming the wCold Dark Matter model, our combined Betti numbers and second moments analysis yields $S_8 = 0.821 \pm 0.018$ and $\Omega _\mathrm{m} = 0.304\pm 0.037$ – constraints 70 per cent tighter than those from cosmic shear two-point statistics in the same parameter plane. Our results demonstrate that topological methods provide a powerful and robust framework for extracting cosmological information, with our spherical methodology readily applicable to upcoming Stage IV wide-field galaxy surveys.

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Publication Details

Journal
Monthly Notices of the Royal Astronomical Society
Published
2025-12-03
DOI
https://doi.org/10.1093/mnras/staf2152
Citations
2
Primary Topic
Topological and Geometric Data Analysis
Type
article
Field-Weighted Citation Impact
3.01

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article

Dark Energy Survey Year 3 results: w CDM cosmology from simulation-based inference with persistent homology on the sphere

A. Roodman, Pratyush Pranav, S. R. Hinton, J Elvin-Poole et al.
2 citations
Monthly Notices of the Royal Astronomical Society
Topological and Geometric Data Analysis
3.01
article

Dark Energy Survey Year 3 results: w CDM cosmology from simulation-based inference with persistent homology on the sphere

A. Roodman, Pratyush Pranav, S. R. Hinton, J Elvin-Poole, M. Gatti, S. Bocquet, S Dodelson, L. N. da Costa, B. Yanny, R Chen, D. Gruen, A. A. Plazas, J. Prat, E. Sheldon, A. Amon, A. Porredon, J. Myles, J. Zuntz, N. MacCrann, G. M. Bernstein, C. Chang, J. Carretero, K. Herner, T. Shin, J. McCullough, S. Pandey, J. Weller, N Jeffrey, J. Mena-Fernández, A. Campos, A. Carnero Rosell, Y. Zhang, J. De Vicente, I Harrison, Marco Raveri, C. To, K. Eckert, A. Ferté, Dhayaa Anbajagane, Mike Jarvis, Joseph DeRose, N Weaverdyck, C. Sánchez, R A Gruendl, S. Lee, R. L. C. Ogando, A. Choi, A. Alarcon, M. E. S. Pereira, S. Everett, R. P. Rollins, H. T. Diehl, S. Desai, F. Andrade-Oliveira, L Whiteway, C. Doux, I. Sevilla-Noarbe, B. X. Santiago, P-F Leget, I. Tutusaus, A. R. Walker, R. Miquel, A Navarro-Alsina, O. Lahav, G Gutierrez, S Allam, D L Hollowood, D. Brooks, V Vikram, E Sanchez, D. Thomas, Joshua A. Frieman, K. Kuehn, D J James, T N Varga, M E C Swanson, C Davis, T. M. C. Abbott, L F Secco, S Samuroff, K. Honscheid, M. Aguena, B. Yin, J García-Bellido, B Flaugher, J. L. Marshall, J Blazek, K Bechtol, M A Troxel, J J Mohr, Erica Huff, Shigeki Sugiyama, R. Cawthon, M Smith, N Kuropatkin, E. Suchyta
article en
2 citations

Abstract

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 $0.3\sigma$) in the $\Omega _\mathrm{m}-S_8$ plane. Assuming the wCold Dark Matter model, our combined Betti numbers and second moments analysis yields $S_8 = 0.821 \pm 0.018$ and $\Omega _\mathrm{m} = 0.304\pm 0.037$ – constraints 70 per cent tighter than those from cosmic shear two-point statistics in the same parameter plane. Our results demonstrate that topological methods provide a powerful and robust framework for extracting cosmological information, with our spherical methodology readily applicable to upcoming Stage IV wide-field galaxy surveys.

Monthly Notices of the Royal Astronomical SocietyVol. 545(3)
Institució Catalana de Recerca i Estudis Avançats (ES), California Institute of Technology (US), Oak Ridge National Laboratory (US), Northeastern University (US), Centre National de la Recherche Scientifique (FR), Universidade Federal do Rio Grande do Sul (BR), Goddard Space Flight Center (US), Fermi National Accelerator Laboratory (US), Institute of Space Sciences (ES), Jet Propulsion Laboratory (US), Université Toulouse III - Paul Sabatier (FR), University of Wisconsin–Madison (US), Lawrence Berkeley National Laboratory (US), University of Waterloo (CA), University of Illinois Urbana-Champaign (US), Universidad de La Laguna (ES), Universität Hamburg (DE), Stockholm University (SE), The University of Queensland (AU), Duke University (US), Université Fédérale de Toulouse Midi-Pyrénées (FR), Universidade Estadual de Campinas (UNICAMP) (BR), Institute for High Energy Physics (ES), Brookhaven National Laboratory (US), Princeton University (US), University of Zurich (CH), University of Cambridge (GB), Lowell Observatory (US), Trieste Astronomical Observatory (IT), Mitchell Institute (US), SLAC National Accelerator Laboratory (US), Australian Astronomical Observatory (AU), University of Manchester (GB), Bennett University (IN), Ashoka University (IN), Central University of Kerala (IN), ETH Zurich (CH), University of Chicago (US), Valongo Observatory (BR), Max Planck Institute for Extraterrestrial Physics (DE), Kavli Institute for Particle Astrophysics and Cosmology (US), Bridge University (SS), Excellence Cluster Origins (DE), Center for Astrophysics Harvard & Smithsonian (US), National Observatory (BR), Istituto Nazionale di Fisica Nucleare, Sezione di Genova (IT), Instituto de Astrofísica de Canarias (ES), National Center for Supercomputing Applications (US), Astronomical Institute of the Slovak Academy of Sciences (SK), Institute for Physics (US), Cerro Tololo Inter-American Observatory (CL), Centro de Tecnologia da Informação Renato Archer (BR), Laboratório Interinstitucional de e-Astronomia (BR), Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (ES), Institut de Recherche en Astrophysique et Planétologie (FR), Institute of Science and Technology, University College London (GB), UK Astronomy Technology Centre (GB), The Ohio State University (US), Stony Brook University (US), William Jewell College (US), University of Portsmouth (GB), Universidad Autónoma de Madrid (ES), Indian Institute of Technology Hyderabad (IN), Lancaster University (GB), Carnegie Mellon University (US), Cardiff University (GB), University of Pennsylvania (US), Ludwig-Maximilians-Universität München (DE), KTH Royal Institute of Technology (SE), Université Grenoble Alpes (FR), Ruhr University Bochum (DE), Texas A&M University (US), University of California, Berkeley (US), Stanford University (US), University of Edinburgh (GB), Macquarie University (AU)
National Science Foundation, U.S. Department of Energy, Ohio State University, University College London, Deutsche Forschungsgemeinschaft, Ministério da Ciência, Tecnologia e Inovação, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Financiadora de Estudos e Projetos, University of Illinois at Urbana-Champaign, Science and Technology Facilities Council, Integrated Electronics Engineering Center, Binghamton University, Argonne National Laboratory, Fermilab
Openalex Percentile: Top 7%
Topological and Geometric Data Analysis
3.01
DOIOpen Access PDFOpenAlex
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