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Record W2783859845 · doi:10.3847/1538-4357/aacce8

Cool-core Clusters: The Role of BCG, Star Formation, and AGN-driven Turbulence

2018· article· en· W2783859845 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsIntracluster mediumTurbulenceJet (fluid)Cluster (spacecraft)Star formationActive galactic nucleusCore (optical fiber)Star cluster

Abstract

fetched live from OpenAlex

Abstract Recent observations of cool cluster cores that include the BCG gravity claim that the observed threshold in min( t cool / t ff ) (cooling time to free-fall time ratio) lies at a somewhat higher value, close to 10–30, compared with the threshold seen in numerical simulations. There are only a few clusters in which this ratio falls much below 10. In this paper, we compare 3D hydrodynamic simulations of feedback active galactic nuclei (AGNs) jets interacting with the intracluster medium, with and without a BCG potential. We find that, for a fixed feedback efficiency, the presence of a BCG does not significantly affect the temperature, but increases (decreases) the core density (entropy) on average. Most importantly, min( t cool / t ff ) is only affected slightly by the inclusion of the BCG gravity. Also notable is that the lowest value of min( t cool / t ff ) in the NFW+BCG runs is about twice as large as in the NFW runs. We also look at the role of depletion of cold gas due to star formation, and show that it only affects the rotationally dominant component, while the radially dominant component remains largely unaffected. Stellar gas depletion also increases the repetition rate of AGN jets. The distribution of metals due to AGN jets in our simulations is predominantly along the jet direction, and the equatorial spread of metals is less compared with the observations. We also show that the turbulence in cool-core clusters is weak, which is consistent with recent Hitomi results on the Perseus cluster.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.838
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.012
GPT teacher head0.257
Teacher spread0.245 · 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 designOther design
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

Citations54
Published2018
Admission routes1
Has abstractyes

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