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Record W3003420132 · doi:10.3847/1538-4357/ab72f0

Estimating the Jet Power of Mrk 231 during the 2017–2018 Flare

2020· article· en· W3003420132 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsVery Long Baseline ArrayBlazarFlareQuasarJet (fluid)LuminosityCore (optical fiber)Flux (metallurgy)

Abstract

fetched live from OpenAlex

Abstract Long-term 17.6 GHz radio monitoring of the broad absorption-line quasar, Mrk 231, detected a strong flare in late 2017. This triggered four epochs of Very Long Baseline Array (VLBA) observations from 8.4 to 43 GHz over a 10 week period as well as an X-ray observation with NuSTAR . This was the third campaign of VLBA monitoring that we have obtained. The 43 GHz VLBA was degraded in all epochs, with only 7 of 10 antennas available in three epochs and 8 in the first epoch. However, useful results were obtained due to a fortuitous capturing of a complete, short 100 mJy flare at 17.6 GHz, both growth and decay. This provided useful constraints on the physical model of the ejected plasma that were not available in previous campaigns. We consider four classes of models: discrete ejections (both protonic and positronic) and jetted (protonic and positronic). The most viable model is a “dissipative bright knot” in a faint background leptonic jet with an energy flux ∼10 43 erg s −1 . Inverse Compton scattering calculations (based on these models) in the ambient quasar photon field explains the lack of a detectable increase in X-ray luminosity measured by NuSTAR . We show that the core (the bright knot) moves toward a nearby secondary at ≈0.97 c . The background jet is much fainter. Evidently, the high-frequency VLBA core does not represent the point of origin of blazar jets, in general, and optical depth “core shift” estimates of jet points of origin can be misleading.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.493
Threshold uncertainty score0.624

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.010
GPT teacher head0.221
Teacher spread0.211 · 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 designBench or experimental
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

Citations9
Published2020
Admission routes1
Has abstractyes

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