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Record W3152858534 · doi:10.3847/1538-4357/abd17b

Constraints on the Mass Accretion Rate onto the Supermassive Black Hole of Cygnus A Using the Submillimeter Array

2021· article· en· W3152858534 on OpenAlexaff
Wen-Ping Lo, Keiichi Asada, Satoki Matsushita, Masanori Nakamura, Hung-Yi Pu, C.-Y. Tseng, Kazunori Akiyama, Juan Carlos Algaba, Geoffrey C. Bower, Ramprasad Rao, Jun Yi Koay, Patrick M. Koch, Shoko Koyama, Paul T. P. Ho, Makoto Inoue

Bibliographic record

VenueThe Astrophysical Journal · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsPerimeter Institute
Fundersnot available
KeywordsPhysicsAstrophysicsSupermassive black holeAccretion (finance)Polarization (electrochemistry)Faraday effectOutflowSubmillimeter ArrayAstronomyMagnetic fieldGalaxyStar formation

Abstract

fetched live from OpenAlex

Abstract We present the first detailed polarimetric studies of Cygnus A at 230 GHz with the Submillimeter Array (SMA) to constrain the mass accretion rate onto its supermassive black hole. We detected the polarized emission associated with the core at a fractional polarization of . This low fractional polarization suggests that the polarized emission is highly depolarized. One of the possible explanations is due to a significant variance in the Faraday rotation measure within the synthesized beam. By assuming the Faraday depolarization caused by inhomogeneous column density of the magnetized plasma associated with the surrounding radiatively-inefficient accretion flow within the SMA beam, we derived the constraint on the mass accretion rate to be larger than 0.15 yr−1 at the Bondi radius. The derived constraint indicates that an adiabatic inflow–outflow solution or an advection-dominated accretion flow should be preferable as the accretion flow model in order to explain the jet power of Cygnus A.

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 score0.002
Threshold uncertainty score0.004

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 science0.0000.000
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 head0.026
GPT teacher head0.247
Teacher spread0.221 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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