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Record W4398157533 · doi:10.1103/physrevd.111.063504

Dark Energy Survey Year 3 results: Simulation-based cosmological inference with wavelet harmonics, scattering transforms, and moments of weak lensing mass maps. II. cosmological results

2025· article· en· W4398157533 on OpenAlexaff
M. Gatti, G. Campailla, N Jeffrey, L Whiteway, A. Porredon, J. Prat, J. Williamson, M Raveri, Bhuvnesh Jain, Virginia Ajani, G. Giannini, M. Yamamoto, Conghao Zhou, J Blazek, Dhayaa Anbajagane, S. Samuroff, Tomasz Kacprzak, A. Alarcon, A. Amon, K. Bechtol, M. R. Becker, G. M. Bernstein, A. Campos, C. Chang, R. Chen, A. Choi, C. Davis, J. Derose, H. T. Diehl, Scott Dodelson, C. Doux, K. Eckert, J. Elvin-Poole, S. Everett, A. Ferté, D. Gruen, R. A. Gruendl, I. Harrison, W G Hartley, K. Herner, E. M. Huff, Mike Jarvis, N. Kuropatkin, P.-F. Léget, N. MacCrann, J. McCullough, J. Myles, A Navarro-Alsina, S. Pandey, R. P. Rollins, A. Roodman, C. Sánchez, L. F. Secco, I. Sevilla-Noarbe, E. Sheldon, T. Shin, M. A. Troxel, I. Tutusaus, T. N. Varga, B. Yanny, B. Yin, Y. Zhang, J. Zuntz, T. Abbott, M. Aguena, S. S. Allam, O. Alves, F. Andrade-Oliveira, D. Bacon, S. Bocquet, David H. Brooks, A. Carnero Rosell, J. Carretero, L. N. da Costa, M. E. S. Pereira, J. De Vicente, I. Ferrero, J. Frieman, J. García-Bellido, E. Gaztañaga, G. Gutiérrez, S. R. Hinton, K. Honscheid, D.J James, K. Kuehn, O. Lahav, S. Lee, J. L. Marshall, J. Mena-Fernández, R. Miquel, A. Pieres, E. Sánchez, D. Sanchez Cid, M. Schubnell, M Smith, E. Suchyta, G. Tarlé, N. Weaverdyck, J. Weller, P. Wiseman

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsRegional Municipality of WaterlooUniversity of WaterlooInstitute of Particle Physics
FundersSLAC National Accelerator LaboratoryEuropean Regional Development FundEuropean Research CouncilScience and Technology Facilities CouncilKavli Institute for Cosmological Physics, University of ChicagoUniversity of Illinois at Urbana-ChampaignLudwig-Maximilians-Universität MünchenFundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de JaneiroCentro de Investigaciones Energéticas, Medioambientales y TecnológicasConselho Nacional de Desenvolvimento Científico e TecnológicoGeneralitat de CatalunyaOffice of ScienceUniversity of EdinburghConsejo Superior de Investigaciones CientíficasCenter for Cosmology and Astroparticle Physics, Ohio State UniversityUniversity of SussexUniversity of CambridgeHigh Energy PhysicsDeutsche ForschungsgemeinschaftArgonne National LaboratoryCentres de Recerca de CatalunyaUniversity of PortsmouthUniversity of ChicagoTexas A and M UniversityInstitut de Física d'Altes EnergiesNational Centre for Supercomputing ApplicationsEidgenössische Technische Hochschule ZürichUniversity College LondonMitchell InstituteUniversity of MichiganUniversity of California, Santa CruzOhio State UniversityMinistério da Ciência, Tecnologia e InovaçãoHigher Education Funding Council for EnglandLawrence Berkeley National LaboratoryFinanciadora de Estudos e ProjetosUniversity of PennsylvaniaMinisterio de Ciencia e InnovaciónEuropean CommissionU.S. Department of EnergyUniversity of NottinghamStanford UniversityFermilabNational Science Foundation
KeywordsPhysicsDark energyWeak gravitational lensingCosmologyRedshiftCold dark matterOmegaGaussianStatisticsAstrophysicsStatistical physicsGalaxyQuantum mechanicsMathematics

Abstract

fetched live from OpenAlex

The paper applies a novel simulation-based cosmological inference methodology to Dark Energy Survey Year-3-based weak-lensing mass maps. Inclusion of nongaussian statistics strongly improves cosmological constraints, establishing consistency with other work with the same and different datasets.

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)0.008
metaresearch head score (Gemma)0.029
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: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.021
GPT teacher head0.350
Teacher spread0.330 · 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

Citations12
Published2025
Admission routes1
Has abstractyes

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