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Record W2811143277 · doi:10.3847/1538-4357/aad086

Probing the Innermost Accretion Flow Geometry of Sgr A* with Event Horizon Telescope

2018· article· en· W2811143277 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsUniversity of WaterlooPerimeter Institute
Fundersnot available
KeywordsEvent horizonBlack hole (networking)Accretion (finance)Spin-flipRadiative transferFlow (mathematics)Intermediate-mass black holeActive galactic nucleusObservable

Abstract

fetched live from OpenAlex

Abstract Upcoming Event Horizon Telescope (EHT) observations will provide a unique opportunity to reveal the innermost region of the radiative inefficient accretion flow (RIAF) around the Galactic black hole, Sgr A*. Depending on the flow dynamics and accumulated magnetic flux, the innermost region of an RIAF could have a quasi-spherical or disk-like geometry. Here we present a phenomenological model to investigate the characteristics of the black hole shadow images with different flow geometries, together with the effect of black hole spin and flow dynamics. The resulting image consists in general of two major components: a crescent, which may surround the funnel region of the black hole or the black hole itself, and a photon ring, which may be partially luminous and overlapped with the crescent component. Compared to a quasi-spherical flow case, a disk-like flow in the vicinity of a black hole exhibits the following image features: (i) due to less material near the funnel region, the crescent structure has a smaller size, and (ii) due to the combination of emission from the flow beside and behind the black hole, the crescent structure has a more irregular shape, and a less smooth brightness distribution. How these features can result in different observables for EHT observations is discussed.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

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 communication0.0010.000
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 head0.010
GPT teacher head0.226
Teacher spread0.217 · 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

Citations40
Published2018
Admission routes1
Has abstractyes

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