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Record W2109343995 · doi:10.1086/421313

Deep CCD Surface Photometry of Galaxy Clusters. II. Searching for Intracluster Starlight in Non‐cD clusters

2004· article· en· W2109343995 on OpenAlexaff
John J. Feldmeier, J. Christopher Mihos, Heather Morrison, Paul Harding, Nathan A. Kaib, John Dubinski

Bibliographic record

VenueThe Astrophysical Journal · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPhysicsAstrophysicsIntracluster mediumGalaxy clusterBrightest cluster galaxyAstronomyPhotometry (optics)Abell 2744GalaxyGalaxy mergerCluster (spacecraft)Galaxy groups and clustersElliptical galaxyStars

Abstract

fetched live from OpenAlex

We report the search for intracluster light in four Abell Type II/III (non-cD) galaxy clusters: Abell 801, 1234, 1553, & 1914. We find on average that these clusters contain $\sim$ 10% of their detected stellar luminosity in a diffuse component. We show that for two of the clusters the intracluster light closely follows the galaxy distribution, but in the other two cases, there are noticeable differences between the spatial distribution of the galaxies and the intracluster light. We report the results of a search for intracluster tidal debris in each cluster, and note that Abell 1914 in particular has a number of strong tidal features likely due to its status as a recent cluster merger. One of the Abell 1914 features appears to be spatially coincident with an extension seen in weak lensing maps, implying the feature traces a large amount of mass. We compare these results to numerical simulations of hierarchically-formed galaxy clusters, and find good general agreement between the observed and simulated images, although we also find that our observations sample only the brightest features of the intracluster light. Together, these results suggest that intracluster light can be a valuable tool in determining the evolutionary state of galaxy clusters.

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 score0.432
Threshold uncertainty score0.791

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 communication0.0000.001
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 head0.230
Teacher spread0.223 · 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

Citations130
Published2004
Admission routes1
Has abstractyes

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