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Record W2604655066 · doi:10.3847/1538-4357/aa6a59

Detection of the Stellar Intracluster Medium in Perseus (Abell 426)

2017· article· en· W2604655066 on OpenAlexaff
William E. Harris, Courtney J. Mulholland

Bibliographic record

VenueThe Astrophysical Journal · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPhysicsAstrophysicsGlobular clusterAstronomyHubble sequenceGalaxyPhotometry (optics)MetallicityHubble space telescopeStellar populationStar clusterElliptical galaxyStar formationStars

Abstract

fetched live from OpenAlex

Abstract Hubble Space Telescope (HST) photometry from the ACS/WFC and WFPC2 cameras is used to detect and measure globular clusters (GCs) in the central region of the rich Perseus cluster of galaxies. A detectable population of Intragalactic GCs is found extending out to at least 500 kpc from the cluster center. These objects display luminosity and color (metallicity) distributions that are entirely normal for GC populations. Extrapolating from the limited spatial coverage of the HST fields, we estimate very roughly that the entire Perseus cluster should contain ∼50,000 or more Intragalactic GCs, but a targetted wide-field survey will be needed for a more definitive answer. Separate brief results are presented for the rich GC systems in NGC 1272 and NGC 1275, the two largest Perseus ellipticals. For NGC 1272, we find a specific frequency of , while for the central giant NGC 1275, we find . In both of these giant galaxies, the GC colors are well matched by bimodal distributions, with the majority in the blue (metal-poor) component. This preliminary study suggests that Perseus is a prime target for a more comprehensive deep imaging survey of Intragalactic GCs.

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)0.000
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.007
GPT teacher head0.211
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 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

Citations9
Published2017
Admission routes1
Has abstractyes

Explore more

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