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Record W3173079094 · doi:10.1103/physrevd.105.023514

Dark Energy Survey Year 3 results: Cosmology from cosmic shear and robustness to data calibration

2022· article· en· W3173079094 on OpenAlexaff
A. Amon, D. Gruen, M. A. Troxel, N. MacCrann, Scott Dodelson, A. Choi, C. Doux, L. F. Secco, S. Samuroff, E. Krause, J. Cordero, J. Myles, Joseph DeRose, Risa H. Wechsler, M. Gatti, A Navarro-Alsina, G. M. Bernstein, Bhuvnesh Jain, J. Blazek, A. Alarcon, A. Ferté, Pablo Lemos, M. Raveri, A. Campos, J. Prat, C. Sánchez, Mike Jarvis, O. Alves, F. Andrade-Oliveira, Eric J. Baxter, K. Bechtol, M. R. Becker, S. L. Bridle, H. Camacho, A. Carnero Rosell, M. Carrasco Kind, R. Cawthon, C. Chang, R. Chen, P. Chintalapati, M. Crocce, C. Davis, H. T. Diehl, A. Drlica-Wagner, K. Eckert, T. F. Eifler, J. Elvin-Poole, S. Everett, Xiao Fang, P. Fosalba, O. Friedrich, E. Gaztañaga, G. Giannini, R. A. Gruendl, I. Harrison, W. G. Hartley, K. Herner, Hung-Jin Huang, E. M. Huff, Dragan Huterer, N. Kuropatkin, P.-F. Léget, Andrew R. Liddle, J. McCullough, J. Muir, Shivam Pandey, Y. Park, A. Porredon, Alexandre Réfrégier, R. P. Rollins, A. Roodman, R. Rosenfeld, Ashley J. Ross, E. S. Rykoff, Javier Sánchez, I. Sevilla-Noarbe, E. Sheldon, T. Shin, A. Troja, I. Tutusaus, T. N. Varga, N. Weaverdyck, B. Yanny, B. Yin, Y. Zhang, J. Zuntz, M. Aguena, S. Allam, J. Annis, D. Bacon, E. Bertin, S. Bhargava, David H. Brooks, E. Buckley‐Geer, D. L. Burke, J. Carretero, M. Costanzi, L. N. da Costa, M. E. S. Pereira, J. De Vicente, S. Desai, J. P. Dietrich, P. Doel, I. Ferrero, B. Flaugher, J. Frieman, J. García-Bellido, D. W. Gerdes, T. Giannantonio, J. Gschwend, G. Gutiérrez, S. R. Hinton, K. Honscheid, B. Hoyle, D. J. James, Richard G. Kron, K. Kuehn, O. Lahav, M. Lima, H. Lin, M. A. G. Maia, J. L. Marshall, Paul Martini, P. Melchior, F. Menanteau, R. Miquel, J. J. Mohr, R. Morgan, R. L. C. Ogando, A. Palmese, F. Paz-Chinchón, D. Petravick, A. Pieres, A. K. Romer, E. Sánchez, V. Scarpine, M. Schubnell, S. Serrano, M. Smith, M. Soares-Santos, G. Tarlé, D. Thomas, C. To, J. Weller

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsPerimeter Institute
FundersSLAC National Accelerator LaboratoryIntegrated Electronics Engineering Center, Binghamton UniversityEuropean Regional Development FundScience and Technology Facilities CouncilEuropean Research CouncilSeventh Framework ProgrammeOffice of ScienceNational Centre for Supercomputing ApplicationsInstitut de Física d'Altes EnergiesCentro de Investigaciones Energéticas, Medioambientales y TecnológicasConselho Nacional de Desenvolvimento Científico e TecnológicoEuropean CommissionMinisterio de Ciencia e InnovaciónGeneralitat de CatalunyaUniversity of Illinois at Urbana-ChampaignLawrence Berkeley National LaboratoryUniversity of PennsylvaniaFinanciadora de Estudos e ProjetosFundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de JaneiroCenter for Cosmology and Astroparticle Physics, Ohio State UniversityUniversity of SussexMinistério da Ciência, Tecnologia e InovaçãoUniversity College LondonNational Aeronautics and Space AdministrationHigher Education Funding Council for EnglandUniversity of PortsmouthTexas A and M UniversityUniversity of ChicagoOhio State UniversityHigh Energy PhysicsDeutsche ForschungsgemeinschaftArgonne National LaboratoryCentres de Recerca de CatalunyaU.S. Department of EnergyFermilabNational Science Foundation
KeywordsPhysicsCosmic microwave backgroundRedshiftAstrophysicsDark energyGalaxyCosmologyPlanckOmegaCOSMIC cancer databasePhotometric redshiftSigmaRedshift surveyAstronomy

Abstract

fetched live from OpenAlex

This work, together with its companion paper, Secco, Samuroff et al. [Phys. Rev. D 105, 023515 (2022)], present the Dark Energy Survey Year 3 cosmic-shear measurements and cosmological constraints based on an analysis of over 100 million source galaxies. With the data spanning $4143\text{ }\text{ }{\mathrm{deg}}^{2}$ on the sky, divided into four redshift bins, we produce a measurement with a signal-to-noise of 40. We conduct a blind analysis in the context of the Lambda-Cold Dark Matter ($\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$) model and find a 3% constraint of the clustering amplitude, ${S}_{8}\ensuremath{\equiv}{\ensuremath{\sigma}}_{8}({\mathrm{\ensuremath{\Omega}}}_{\mathrm{m}}/0.3{)}^{0.5}=0.75{9}_{\ensuremath{-}0.023}^{+0.025}$. A $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$-Optimized analysis, which safely includes smaller scale information, yields a 2% precision measurement of ${S}_{8}=0.77{2}_{\ensuremath{-}0.017}^{+0.018}$ that is consistent with the fiducial case. The two low-redshift measurements are statistically consistent with the Planck Cosmic Microwave Background result, however, both recovered ${S}_{8}$ values are lower than the high-redshift prediction by $2.3\ensuremath{\sigma}$ and $2.1\ensuremath{\sigma}$ ($p$-values of 0.02 and 0.05), respectively. The measurements are shown to be internally consistent across redshift bins, angular scales and correlation functions. The analysis is demonstrated to be robust to calibration systematics, with the ${S}_{8}$ posterior consistent when varying the choice of redshift calibration sample, the modeling of redshift uncertainty and methodology. Similarly, we find that the corrections included to account for the blending of galaxies shifts our best-fit ${S}_{8}$ by $0.5\ensuremath{\sigma}$ without incurring a substantial increase in uncertainty. We examine the limiting factors for the precision of the cosmological constraints and find observational systematics to be subdominant to the modeling of astrophysics. Specifically, we identify the uncertainties in modeling baryonic effects and intrinsic alignments as the limiting systematics.

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)0.014
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.033
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.025
GPT teacher head0.354
Teacher spread0.329 · 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 designObservational
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

Citations425
Published2022
Admission routes1
Has abstractyes

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