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Record W2070782828 · doi:10.1051/0004-6361/201322447

Mass profile and dynamical status of the<i>z</i>~ 0.8 galaxy cluster LCDCS 0504

2014· article· en· W2070782828 on OpenAlexaff
L. Guennou, A. Biviano, C. Adami, Marceau Limousin, G. B. Lima Neto, G. A. Mamon, M. P. Ulmer, R. Gavazzi, E. S. Cypriano, F. Durret, Douglas Clowe, V. LeBrun, S. Allam, S. Basa, C. Benoist, A. Cappi, C. Halliday, O. Ilbert, David Johnston, Eric Jullo, Dennis W. Just, Jeffrey M. Kubo, I. Márquez, Philip J. Marshall, N. Martinet, S. Maurogordato, A. Mazure, Kellen Murphy, H. Plana, F. Rostagni, D. Russeil, M. Schirmer, T. Schrabback, E. Slezak, D. L. Tucker, Dennis Zaritsky, B. Ziegler

Bibliographic record

VenueAstronomy and Astrophysics · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsUniversity of Toronto
FundersComisión Nacional de Investigación Científica y TecnológicaAustralian Research CouncilCentre National de la Recherche ScientifiqueConselho Nacional de Desenvolvimento Científico e TecnológicoMinisterio de Ciencia, Tecnología e Innovación ProductivaMinistério da Ciência, Tecnologia e InovaçãoNational Aeronautics and Space AdministrationCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorJet Propulsion LaboratoryCentre National d’Etudes SpatialesFundação de Amparo à Pesquisa do Estado de São PauloAgence Nationale de la RechercheSpace Telescope Science InstituteScience and Technology Facilities CouncilNational Research FoundationDanmarks GrundforskningsfondCalifornia Institute of TechnologyNational Science Foundation
KeywordsCluster (spacecraft)Context (archaeology)AstrophysicsGalaxy clusterGalaxyRedshiftPhysicsAstronomyGeographyComputer science

Abstract

fetched live from OpenAlex

Context. Constraints on the mass distribution in high-redshift clusters of galaxies are currently not very strong.Aims. We aim to constrain the mass profile, M(r), and dynamical status of the z ~ 0.8 LCDCS 0504 cluster of galaxies that is characterized by prominent giant gravitational arcs near its center.Methods. Our analysis is based on deep X-ray, optical, and infrared imaging as well as optical spectroscopy, collected with various instruments, which we complemented with archival data. We modeled the mass distribution of the cluster with three different mass density profiles, whose parameters were constrained by the strong lensing features of the inner cluster region, by the X-ray emission from the intracluster medium, and by the kinematics of 71 cluster members.Results. We obtain consistent M(r) determinations from three methods based on kinematics (dispersion-kurtosis, caustics, and MAMPOSSt), out to the cluster virial radius, ≃1.3 Mpc and beyond. The mass profile inferred by the strong lensing analysis in the central cluster region is slightly higher than, but still consistent with, the kinematics estimate. On the other hand, the X-ray based M(r) is significantly lower than the kinematics and strong lensing estimates. Theoretical predictions from ΛCDM cosmology for the concentration–mass relation agree with our observational results, when taking into account the uncertainties in the observational and theoretical estimates. There appears to be a central deficit in the intracluster gas mass fraction compared with nearby clusters.Conclusions. Despite the relaxed appearance of this cluster, the determinations of its mass profile by different probes show substantial discrepancies, the origin of which remains to be determined. The extension of a dynamical analysis similar to that of other clusters of the DAFT/FADA survey with multiwavelength data of sufficient quality will allow shedding light on the possible systematics that affect the determination of mass profiles of high-z clusters, which is possibly related to our incomplete understanding of intracluster baryon physics.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.003
GPT teacher head0.180
Teacher spread0.177 · 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

Citations16
Published2014
Admission routes1
Has abstractyes

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