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Record W4285045546 · doi:10.3847/1538-4357/ac7466

Deep Very Long Baseline Interferometry Observations Challenge Previous Evidence of a Binary Supermassive Black Hole Residing in Seyfert Galaxy NGC 7674

2022· article· en· W4285045546 on OpenAlexaff
Peter Breiding, Sarah Burke-Spolaor, Tao An, Karishma Bansal, P. Mohan, Gregory B. Taylor, Yingkang Zhang

Bibliographic record

VenueThe Astrophysical Journal · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadio Astronomy Observations and Technology
Canadian institutionsCanadian Institute for Advanced Research
Fundersnot available
KeywordsSupermassive black holeVery-long-baseline interferometryPhysicsAstrophysicsVery Long Baseline ArrayGalaxyActive galactic nucleusBinary black holeBinary numberAstronomyGravitational wave

Abstract

fetched live from OpenAlex

Abstract Previous Ku -band (15 GHz) imaging with data obtained from the Very Long Baseline Array (VLBA) had shown two compact, subparsec components at the location of a presumed kiloparsec-scale radio core in Seyfert galaxy NGC 7674. It was then presumed that these two unresolved and compact components were dual radio cores corresponding to two supermassive black holes (SMBHs) accreting surrounding gas and launching radio-bright relativistic jets. However, utilizing the original VLBA data set used to claim the detection of a binary SMBH, in addition to later multiepoch/multifrequency data sets obtained from both the VLBA and the European very long baseline interferometry (VLBI) network, we find no evidence to support the presence of a binary SMBH. We place stringent upper limits to the flux densities of any subparsec-scale radio cores that are at least an order of magnitude lower than the original VLBI radio-core detections, directly challenging the original binary SMBH detection claim. With this in mind, we discuss the possible reasons for the nondetection of any VLBI radio cores in our imaging, the possibility of a binary SMBH still residing in NGC 7674, and the prospect of future observations shedding further light on the true nature of this active galactic nucleus.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score0.699

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.032
GPT teacher head0.261
Teacher spread0.228 · 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 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

Citations7
Published2022
Admission routes1
Has abstractyes

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