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Record W2002610082 · doi:10.1088/2041-8205/721/1/l19

SUPERDENSE MASSIVE GALAXIES IN THE ESO DISTANT CLUSTER SURVEY (EDisCS)

2010· article· en· W2002610082 on OpenAlexaff
T. Valentinuzzi, B. M. Poggianti, Rp Saglia, Alfonso Aragón‐Salamanca, L. Simard, P. Sánchez–Blázquez, M. D’Onofrio, A. Cava, WJ Couch, J. Fritz, A. Moretti, Benedetta Vulcani

Bibliographic record

VenueThe Astrophysical Journal Letters · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsHerzberg Institute of Astrophysics
FundersScience and Technology Facilities Council
KeywordsAstrophysicsPhysicsGalaxy clusterBrightest cluster galaxyGalaxyElliptical galaxyCluster (spacecraft)AstronomyRedshiftGalaxy groupStellar massStar formation

Abstract

fetched live from OpenAlex

We find a significant number of massive and compact galaxies in clusters from the ESO Distant Clusters Survey (EDisCS) at 0.4 < z < 1. They have similar stellar masses, ages, sizes, and axial ratios to local z ~ 0.04 compact galaxies in WIde field Nearby Galaxy clusters Survey (WINGS) clusters, and to z = 1.4-2 massive and passive galaxies found in the general field. If non-brightest cluster galaxies of all densities, morphologies, and spectral types are considered, the median size of EDisCS galaxies is only a factor 1.18 smaller than in WINGS. We show that for morphologically selected samples, the morphological evolution taking place in a significant fraction of galaxies during the last Gyr may introduce an apparent, spurious evolution of size with redshift, which is actually due to intrinsic differences in the selected samples. We conclude that the median mass-size relation of cluster galaxies does not evolve significantly from z ~ 0.7 to z ~ 0.04. In contrast, the masses and sizes of BCGs and galaxies with M *>4 × 1011 M sun have significantly increased by a factor of 2 and 4, respectively, confirming the results of a number of recent works on the subject. Our findings show that progenitor bias effects play an important role in the size-growth paradigm of massive and passive galaxies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.026
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.209
Teacher spread0.203 · 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 teacher head, 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

Citations85
Published2010
Admission routes1
Has abstractyes

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