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Record W3101988815

Evidence for Rapid Redshift Evolution of Strong Cluster Cooling Flows

2016· article· en· W3101988815 on OpenAlexaff
Rocco Samuele, A. Vikhlinin, C. R. Mullis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsPerimeter InstituteUniversity of Waterloo
Fundersnot available
KeywordsAstrophysicsPhysicsROSATRedshiftCooling flowGalaxyCluster (spacecraft)LuminosityGalaxy clusterBrightest cluster galaxyAstronomyEmission spectrumLine (geometry)Doubly ionized oxygenSpectral lineGeometry
DOInot available

Abstract

fetched live from OpenAlex

We present equivalent widths of the [OII]λ3727 and Hα nebular emission lines for 77 brightest cluster galaxies (BCGs) selected from the 160 Square Degree ROSAT X-ray survey. We find no [OII]λ3727 or Hα emission stronger than −15 A ̊ or −5 Å, respectively, in any BCG. The corresponding emission line luminosities lie below L ∼ 6×1040 erg s−1, which is a factor of 30 below that of NGC1275 in the Perseus cluster. A comparison to the detection frequency of nebular emission in BCGs at z ∼< 0.35 drawn from the Brightest Cluster Survey (Crawford et al. 1999) indicates that we should have detected roughly one dozen emission-line galaxies, assuming the two surveys are selecting similar clusters in the X-ray luminosity range 1042 erg s−1 to 1045 erg s−1. The absence of luminous nebular emission (ie., Perseus-like systems) in our sample is consistent with an increase in the number density of strong cooling flow (cooling core) clusters between z = 0.5 and today. The decline in their numbers at higher redshift could be due to cluster mergers and AGN heating.

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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.255
Teacher spread0.229 · 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

Citations25
Published2016
Admission routes1
Has abstractyes

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