SPT Clusters with DES and HST Weak Lensing. I. Cluster Lensing and Bayesian Population Modeling of Multi-Wavelength Cluster Datasets

• Main Authors: Bocquet, S, Grandis, S, Bleem, L.E, Klein, M, Mohr, J.J, Aguena, M, Alarcon, A, Allam, S, Allen, S.W, Alves, O, Amon, A, Ansarinejad, B, Bacon, D, Bayliss, M, Bechtol, K, Becker, M.R, Benson, B.A, Bernstein, G.M, Brodwin, M, Brooks, D, Campos, A, Canning, R.E.A, Carlstrom, J.E, Carnero Rosell, A, Carrasco Kind, M, Carretero, J, Cawthon, R, Chang, C, Chen, R, Choi, A, Cordero, J, Costanzi, M, da Costa, L.N, Pereira, M.E.S, Davis, C, Derose, J, Desai, S, de Haan, T, de Vicente, J, Diehl, H.T, Dodelson, S, Doel, P, Doux, C, Drlica-Wagner, A, Eckert, K, Elvin-Poole, J, Everett, S, Ferrero, I, Ferté, A, Flores, A.M, Frieman, J, García-Bellido, J, Gatti, M, Giannini, G, Gladders, M.D, Gruen, D, Gruendl, R.A, Harrison, I, Hartley, W.G, Herner, K, Hinton, S.R, Hollowood, D.L, Holzapfel, W.L, Honscheid, K, Huang, N, Huff, E.M, James, D.J, Jarvis, M, Khullar, G, Kim, K, Kraft, R, Kuehn, K, Kuropatkin, N, Kéruzoré, F, Lee, S, Leget, P.-F, Maccrann, N, Mahler, G, Mantz, A, Marshall, J.L, Mccullough, J, Mcdonald, M, Mena-Fernández, J, Miquel, R, Myles, J, Navarro-Alsina, A, Ogando, R.L.C, Palmese, A, Pandey, S, Pieres, A, Plazas Malagón, A.A, Prat, J, Raveri, M, Reichardt, C.L, Roberson, J, Rollins, R.P, Romer, A.K, Romero, C, Roodman, A, Ross, A.J, Rykoff, E.S, Salvati, L, Sánchez, C, Sanchez, E, Sanchez Cid, D, Saro, A, Schrabback, T, Schubnell, M, Secco, L.F, Sevilla-Noarbe, I, Sharon, K, Sheldon, E, Shin, T, Smith, M, Somboonpanyakul, T, Stalder, B, Stark, A.A, Strazzullo, V, Suchyta, E, Swanson, M.E.C, Tarle, G, To, C, Troxel, M.A, Tutusaus, I, Varga, T.N, von der Linden, A, Weaverdyck, N, Weller, J, Wiseman, P, Yanny, B, Yin, B, Young, M, Zhang, Y, Zuntz, J
• Other Authors: Laboratoire de Physique Subatomique et de Cosmologie (LPSC), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Institut d'astrophysique spatiale (IAS), Université Paris-Sud - Paris 11 (UP11)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National d’Études Spatiales Paris (CNES), Institut de recherche en astrophysique et planétologie (IRAP), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Centre National de la Recherche Scientifique (CNRS), DES, SPT
• Format: Report
• Language: English
• Published: HAL CCSD 2023
• Subjects: [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph]; Hubble; South Pole; South pole
• Online Access: https://hal.science/hal-04266477

International audience We present a Bayesian population modeling method to analyze the abundance of galaxy clusters identified by the South Pole Telescope (SPT) with a simultaneous mass calibration using weak gravitational lensing data from the Dark Energy Survey (DES) and the Hubble Space Telescope (HST). We discuss and validate the modeling choices with a particular focus on a robust, weak-lensing-based mass calibration using DES data. For the DES Year 3 data, we report a systematic uncertainty in weak-lensing mass calibration that increases from 1% at $z=0.25$ to 10% at $z=0.95$, to which we add 2% in quadrature to account for uncertainties in the impact of baryonic effects. We implement an analysis pipeline that joins the cluster abundance likelihood with a multi-observable likelihood for the SZ, optical richness, and weak-lensing measurements for each individual cluster. We validate that our analysis pipeline can recover unbiased cosmological constraints by analyzing mocks that closely resemble the cluster sample extracted from the SPT-SZ, SPTpol~ECS, and SPTpol~500d surveys and the DES Year~3 and HST-39 weak-lensing datasets. This work represents a crucial prerequisite for the subsequent cosmological analysis of the real dataset.