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Record W4403091086 · doi:10.1103/physrevd.110.083509

SPT clusters with DES and HST weak lensing. I. Cluster lensing and Bayesian population modeling of multiwavelength cluster datasets

2024· article· en· W4403091086 on OpenAlexaff
S. Bocquet, S. Grandis, L. E. Bleem, Matthias Klein, J. J. Mohr, M. Aguena, A. Alarcon, S. Allam, S. W. Allen, O. Alves, A. Amon, Behzad Ansarinejad, David Bacon, M. Bayliss, K. Bechtol, M. R. Becker, B. A. Benson, G. M. Bernstein, M. Brodwin, D. Brooks, A. Campos, Rebecca Canning, J. E. Carlstrom, A. Carnero Rosell, M. Carrasco Kind, J. Carretero, R. Cawthon, C. Chang, R Chen, A. Choi, J. Cordero, M. Costanzi, L. N. da Costa, M. E. S. Pereira, C. Davis, J. DeRose, S. Desai, T. de Haan, J. De Vicente, H. T. Diehl, Scott Dodelson, P. Doel, C. Doux, A. Drlica-Wagner, K. Eckert, J. Elvin-Poole, S. Everett, I. Ferrero, A. Ferté, A. M. Flores, Joshua A. Frieman, J. García-Bellido, M. Gatti, G. Giannini, Michael D. Gladders, D. Gruen, R. A. Gruendl, I. Harrison, W. G. Hartley, K. Herner, S. R. Hinton, W. L. Holzapfel, K. Honscheid, N. Huang, E. M. Huff, D. J. James, Mike Jarvis, Gourav Khullar, Keunho Kim, Robert P. Kraft, K. Kuehn, N.P. Kuropatkin, F. Kéruzoré, S. Lee, P.-F. Léget, N. MacCrann, Guillaume Mahler, A. Mantz, J. L. Marshall, J. McCullough, M. McDonald, J. Mena-Fernández, R. Miquel, J. Myles, A Navarro-Alsina, R. L. C. Ogando, A. Palmese, S Pandey, A. Pieres, J. Prat, M. Raveri, C. L. Reichardt, Joseph Roberson, R. P. Rollins, A. K. Romer, C. Romero, A. Roodman, Ashley J. Ross, E. S. Rykoff, L. Salvati, C. Sánchez, E. Sánchez, D. Sanchez Cid, A. Saro, T. Schrabback, M. Schubnell, L F Secco, I. Sevilla-Noarbe, K. Sharon, E. Sheldon, T. Shin, M. Smith, Taweewat Somboonpanyakul, B. Stalder, A. A. Stark, V. Strazzullo, E. Suchyta, M. E. C. Swanson, G. Tarlé, C. To, M. A. Troxel, I. Tutusaus, T. N. Varga, Anja von der Linden, N. Weaverdyck, J. Weller, P. Wiseman, B. Yanny, B. Yin, M. R. Young, Y. Zhang, J. Zuntz

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsInstitute of Particle PhysicsUniversity of TorontoUniversity of Waterloo
FundersSLAC National Accelerator LaboratoryArgonne National LaboratoryEuropean Regional Development FundAustralian Research CouncilEuropean Research CouncilScience and Technology Facilities CouncilKavli Institute for Cosmological Physics, University of ChicagoBundesministerium für Wirtschaft und EnergieUniversity of Illinois at Urbana-ChampaignMinisterio de Ciencia y TecnologíaLudwig-Maximilians-Universität MünchenMinisterio de Ciencia e InnovaciónFundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de JaneiroCentro de Investigaciones Energéticas, Medioambientales y TecnológicasInstituto Nacional de Ciência e Tecnologia: Física Nuclear e AplicaçõesConselho Nacional de Desenvolvimento Científico e TecnológicoHigh Energy PhysicsDeutsche ForschungsgemeinschaftGeneralitat de CatalunyaOffice of ScienceUniversity of EdinburghUniversity of SussexInstitut de Física d'Altes EnergiesEidgenössische Technische Hochschule ZürichUniversity College LondonUniversity of CambridgeUniversity of PortsmouthUniversity of ChicagoTexas A and M UniversityMax-Planck-GesellschaftUniversity of MichiganOhio State UniversityDeutsches Zentrum für Luft- und RaumfahrtBundesministerium für Klimaschutz, Umwelt, Energie, Mobilität, Innovation und TechnologieMinistério da Ciência, Tecnologia e InovaçãoLawrence Berkeley National LaboratoryFinanciadora de Estudos e ProjetosUniversity of PennsylvaniaUniversity of NottinghamStanford UniversityU.S. Department of EnergyÖsterreichische ForschungsförderungsgesellschaftUniversity of CaliforniaFermilabNational Science Foundation
KeywordsCluster (spacecraft)Strong gravitational lensingWeak gravitational lensingAstrophysicsPhysicsComputer scienceGalaxyRedshift

Abstract

fetched live from OpenAlex

In these two papers, the authors use mock maps to establish a method for studying the abundance and calibrating the weak-lensing based mass of galaxy clusters. They set up a likelihood function, thus obtaining cosmological constraints from a sample of 1,005 clusters detected with the South Pole Telescope, in combination with further cluster data from the Dark Energy Survey, the Wide-field Infrared Survey Explorer, and the Hubble Space Telescope.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.338
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations27
Published2024
Admission routes1
Has abstractyes

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