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Record W2963079296 · doi:10.1002/asna.201612347

Driving extreme variability: Measuring the evolving coronæ and evidence for jet launching in AGN

2016· article· en· W2963079296 on OpenAlexaff
Dan Wilkins

Bibliographic record

VenueAstronomische Nachrichten · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsPhysicsAstrophysicsActive galactic nucleusSupermassive black holeAccretion (finance)Corona (planetary geology)AstronomyGalaxyAccretion discQuasar

Abstract

fetched live from OpenAlex

Abstract Relativistically blurred reflection from the accretion disc provides a powerful probe of the extreme environments close to supermassive black holes; the inner regions of the accretion flow and the corona that produces the intense X‐ray continuum. Techniques by which the geometry and extent of the corona can be measured through the observed X‐ray spectrum are reviewed along with the evolution in the structure of the corona that is seen to accompany variations in the X‐ray luminosity both on long and short timescales. Detailed analyses of the narrow line Seyfert 1 galaxies Mrk 335 and 1H 0707–495, over observations with XMM‐ Newton as well as Suzaku and NuSTAR spanning nearly a decade reveal that increases in the X‐ray luminosity coincide with an expansion of the corona to cover a larger area of the inner accretion disc. Underlying this long timescale variability lie more complex patterns of behaviour on short timescales. Flares in the X‐ray emission during a low flux state of Mrk 335 observed in 2013 and 2014 are found to mark a reconfiguration of the corona while there is evidence that the flares were caused by a vertical collimation and ejection of coronal material, reminiscent of an aborted jet‐launching event. Measurements of the corona and reflecting accretion disc are combined to infer the conditions on the inner disc that lead to the flaring event. (© 2016 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.056
GPT teacher head0.262
Teacher spread0.207 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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