Selection function of clusters in Dark Energy Survey year 3 data from cross-matching with South Pole Telescope detections

Context. Galaxy clusters selected based on overdensities of galaxies in photometric surveys provide the largest cluster samples. However, modeling the selection function of such samples is complicated by noncluster members projected along the line of sight (projection effects) and the potential detection of unvirialized objects (contamination). Aims. We empirically constrained the magnitude of these effects by cross-matching galaxy clusters selected in the Dark Energy Survey data with the redMaPPer algorithm with significant detections in three South Pole Telescope surveys (SZ, pol-ECS, pol-500d). Methods. For matched clusters, we augmented the redMaPPer catalog with the SPT detection significance. For unmatched objects we used the SPT detection threshold as an upper limit on the SZe signature. Using a Bayesian population model applied to the collected multiwavelength data, we explored various physically motivated models to describe the relationship between observed richness and halo mass. Results. Our analysis reveals a clear preference for models with an additional skewed scatter component associated with projection effects over a purely log-normal scatter model. We rule out significant contamination by unvirialized objects at the high-richness end of the sample. While dedicated simulations offer a well-fitting calibration of projection effects, our findings suggest the presence of redshift-dependent trends that these simulations may not have captured. Our findings highlight that modeling the selection function of optically detected clusters remains a complicated challenge that requires a combination of simulation and data-driven approaches.

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Publication Details

Journal
Astronomy and Astrophysics
Published
2025-06-19
DOI
https://doi.org/10.1051/0004-6361/202554177
Citations
2
Primary Topic
Advanced Statistical Methods and Models
Type
article
Field-Weighted Citation Impact
5.26

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article

Selection function of clusters in Dark Energy Survey year 3 data from cross-matching with South Pole Telescope detections

E. Gaztanaga, J. Weller, J. García-Bellido, C. L. Reichardt et al.
2 citations
Astronomy and Astrophysics
Advanced Statistical Methods and Models
5.26
article

Selection function of clusters in Dark Energy Survey year 3 data from cross-matching with South Pole Telescope detections

E. Gaztanaga, J. Weller, J. García-Bellido, C. L. Reichardt, J. L. Marshall, S. Everett, L. N. da Costa, E Sanchez, A. A. Plazas, Gregory Tarlé, M. Soares-Santos, D. Sanchez Cid, E. Suchyta, S.R. Hinton, C. To, S. Bocquet, P. Wiseman, L. E. Bleem, S. Grandis, N. Weaverdyck, J. Frieman, A. Palmese, D. Gruen, Martin W. Sommer, J. Carretero, B. Flaugher, S. Samuroff, T. Davis, F. Andrade-Oliveira, M. Costanzi, J. Mena-Fernández, A. Carnero Rosell, G. Gutierrez, H. T. Diehl, M. Aguena, J. García-Bellido, J. J. Mohr, R. Miquel, D.J. James, R. A. Gruendl, K. Honscheid, M. E. S. Pereira, David H. Brooks, D. L. Tucker, Hao‐Yi Wu, Matthias Klein, P. Doel, B. X. Santiago, M. Smith, A. Saro, I Sevilla-Noarbe, S. Allam, A. Kathy Romer, S. D. Desai, J. Hlacacek-Larrondo
article en
2 citations

Abstract

Context. Galaxy clusters selected based on overdensities of galaxies in photometric surveys provide the largest cluster samples. However, modeling the selection function of such samples is complicated by noncluster members projected along the line of sight (projection effects) and the potential detection of unvirialized objects (contamination). Aims. We empirically constrained the magnitude of these effects by cross-matching galaxy clusters selected in the Dark Energy Survey data with the redMaPPer algorithm with significant detections in three South Pole Telescope surveys (SZ, pol-ECS, pol-500d). Methods. For matched clusters, we augmented the redMaPPer catalog with the SPT detection significance. For unmatched objects we used the SPT detection threshold as an upper limit on the SZe signature. Using a Bayesian population model applied to the collected multiwavelength data, we explored various physically motivated models to describe the relationship between observed richness and halo mass. Results. Our analysis reveals a clear preference for models with an additional skewed scatter component associated with projection effects over a purely log-normal scatter model. We rule out significant contamination by unvirialized objects at the high-richness end of the sample. While dedicated simulations offer a well-fitting calibration of projection effects, our findings suggest the presence of redshift-dependent trends that these simulations may not have captured. Our findings highlight that modeling the selection function of optically detected clusters remains a complicated challenge that requires a combination of simulation and data-driven approaches.

Astronomy and AstrophysicsVol. 700
Institució Catalana de Recerca i Estudis Avançats (ES), California Institute of Technology (US), Argonne National Laboratory (US), Oak Ridge National Laboratory (US), Northeastern University (US), Universidade Federal do Rio Grande do Sul (BR), Fermi National Accelerator Laboratory (US), Institute of Space Sciences (ES), University of Bonn (DE), Lawrence Berkeley National Laboratory (US), University of Illinois Urbana-Champaign (US), Universidad de La Laguna (ES), Universität Hamburg (DE), University of Sussex (GB), The University of Queensland (AU), The University of Melbourne (AU), Southern Methodist University (US), Institute for High Energy Physics (ES), Universität Innsbruck (AT), University of Zurich (CH), University of Michigan (US), Trieste Astronomical Observatory (IT), Mitchell Institute (US), SLAC National Accelerator Laboratory (US), CEA Grenoble (FR), University of Chicago (US), Max Planck Institute for Extraterrestrial Physics (DE), Institut d'Estudis Espacials de Catalunya (ES), Kavli Institute for Particle Astrophysics and Cosmology (US), Center for Astrophysics Harvard & Smithsonian (US), Instituto de Astrofísica de Canarias (ES), National Center for Supercomputing Applications (US), Laboratório Interinstitucional de e-Astronomia (BR), Institute for Fundamental Physics of the Universe (IT), Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (ES), University of Southampton (GB), University College London (GB), The Ohio State University (US), University of Portsmouth (GB), Universidad Autónoma de Madrid (ES), Indian Institute of Technology Hyderabad (IN), Lancaster University (GB), Université de Montréal (CA), Carnegie Mellon University (US), Ludwig-Maximilians-Universität München (DE), Texas A&M University (US), University of California, Berkeley (US)
National Science Foundation, U.S. Department of Energy, University of Pennsylvania, Ohio State University, University of Chicago, University of Portsmouth, Centres de Recerca de Catalunya, University College London, European Commission, University of Sussex, Deutsche Forschungsgemeinschaft, Generalitat de Catalunya, Ministério da Ciência, Tecnologia e Inovação, Conselho Nacional de Desenvolvimento Científico e Tecnológico, China Scholarship Council, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Financiadora de Estudos e Projetos, Ministerio de Ciencia e Innovación, Institut de Física d'Altes Energies, University of Illinois at Urbana-Champaign, Office of Science, Science and Technology Facilities Council, European Regional Development Fund, High Energy Physics, Integrated Electronics Engineering Center, Binghamton University, Argonne National Laboratory, Fermilab, Lawrence Berkeley National Laboratory, SLAC National Accelerator Laboratory
Openalex Percentile: Top 5%
Advanced Statistical Methods and Models
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