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Record W2161177702 · doi:10.1017/s174392131500201x

AGN feedback by relativistic jets

2014· article· en· W2161177702 on OpenAlexaff
G. V. Bicknell, B. R. McNamara, M. A. Nawaz, Ralph S. Sutherland, Masayuki Umemura, A. Y. Wagner

Bibliographic record

VenueProceedings of the International Astronomical Union · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPhysicsAstrophysicsAstrophysical jetRadio galaxyGalaxyQuasarPrecessionJet (fluid)Relativistic beamingCooling flowRedshiftBlack hole (networking)LuminosityElliptical galaxyAstronomyActive galactic nucleusMechanics

Abstract

fetched live from OpenAlex

Abstract Feedback provided by relativistic jets may be effective in shaping the galaxy luminosity function. The quenching mode (quasar mode) at redshifts ~2-3 potentially disperses gas in star-forming galaxies. The maintenance mode (radio mode) heats the gas in galaxy clusters counteracting cooling flows. A number of authors have examined the effect of relativistic jets in dispersing clouds in the kpc-scale inhomogeneous interstellar medium of evolving galaxies. We have also investigated a particular case of maintenance-mode feedback in our simulation of the iconic radio galaxy / cooling flow cluster Hydra A. Modelling of the knots produced by the jets in the inner 10 kpc provides an estimate of 0.8 – 0.9 c for the velocities of the jets in agreement with other velocity estimates for FR1 jets. The addition of jet precession provides realistic simulations of the morphology of the Hydra A radio source and raises interesting questions as to the role of black hole and disk precession, in general, in galaxy formation.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.005
GPT teacher head0.188
Teacher spread0.184 · 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 designSimulation or modeling
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

Citations0
Published2014
Admission routes1
Has abstractyes

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