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Record W2135959397 · doi:10.1086/588212

An Infrared Survey of Brightest Cluster Galaxies. II. Why are Some Brightest Cluster Galaxies Forming Stars?

2008· article· en· W2135959397 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsUniversity of Waterloo
FundersUniversity of RochesterCalifornia Institute of TechnologyJet Propulsion LaboratoryNational Aeronautics and Space Administration
KeywordsStar formationLuminosityGalaxyCluster (spacecraft)InfraredGalaxy clusterSpitzer Space TelescopeIntracluster mediumLuminous infrared galaxy

Abstract

fetched live from OpenAlex

Quillen et al. presented an imaging survey with the Spitzer Space Telescope of 62 brightest cluster galaxies with optical line emission located in the cores of X-ray-luminous clusters. They found that at least half of these sources have signs of excess IR emission. Here we discuss the nature of the IR emission and its implications for cool core clusters. The strength of the mid-IR excess emission correlates with the luminosity of the optical emission lines. Excluding the four systems dominated by an AGN, the excess mid-IR emission in the remaining brightest cluster galaxies is likely related to star formation. The mass of molecular gas (estimated from CO observations) is correlated with the IR luminosity as found for normal star-forming galaxies. The gas depletion timescale is about 1 Gyr. The physical extent of the IR excess is consistent with that of the optical emission-line nebulae. This supports the hypothesis that star formation occurs in molecular gas associated with the emission-line nebulae and with evidence that the emission-line nebulae are mainly powered by ongoing star formation. We find a correlation between mass deposition rates () estimated from the X-ray emission and the star formation rates estimated from the IR luminosity. The star formation rates are 1/10 to 1/100 of the mass deposition rates, suggesting that the reheating of the intracluster medium is generally very effective in reducing the amount of mass cooling from the hot phase but not eliminating it completely.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.002
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 head0.014
GPT teacher head0.220
Teacher spread0.206 · 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.

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

Citations272
Published2008
Admission routes1
Has abstractyes

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