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Record W2526698545 · doi:10.3847/1538-4357/aa73d4

Accretion in Radiative Equipartition (AiRE) Disks

2017· article· en· W2526698545 on OpenAlexaff
Yasaman K. Yazdi, Niayesh Afshordi

Bibliographic record

VenueThe Astrophysical Journal · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsPerimeter InstituteUniversity of Waterloo
Fundersnot available
KeywordsEquipartition theoremPhysicsAccretion (finance)AstrophysicsRadiative transferRadiation pressureSupermassive black holeAccretion discCosmic timeRadiationEddington luminosityInstabilityRadiative coolingMechanicsActive galactic nucleusStar formationGalaxyQuantum mechanicsMagnetic field

Abstract

fetched live from OpenAlex

Abstract Standard accretion disk theory predicts that the total pressure in disks at typical (sub-)Eddington accretion rates becomes radiation pressure dominated. However, radiation pressure dominated disks are thermally unstable. Since these disks are observed in approximate steady state over the instability timescale, our accretion models in the radiation-pressure-dominated regime (i.e., inner disk) need to be modified. Here, we present a modification to the Shakura & Sunyaev model, where the radiation pressure is in equipartition with the gas pressure in the inner region. We call these flows accretion in radiative equipartition (AiRE) disks. We introduce the basic features of AiRE disks and show how they modify disk properties such as the Toomre parameter and the central temperature. We then show that the accretion rate of AiRE disks is limited from above and below, by Toomre and nodal sonic point instabilities, respectively. The former leads to a strict upper limit on the mass of supermassive black holes as a function of cosmic time (and spin), while the latter could explain the transition between hard and soft states of X-ray binaries.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.000
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 head0.024
GPT teacher head0.280
Teacher spread0.256 · 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 designTheoretical or conceptual
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

Citations1
Published2017
Admission routes1
Has abstractyes

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