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Record W1487858455 · doi:10.1088/0004-637x/703/2/l104

SIGNATURES OF RELATIVISTIC HELICAL MOTION IN THE ROTATION MEASURES OF ACTIVE GALACTIC NUCLEUS JETS

2009· article· en· W1487858455 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsCanadian Institute for Theoretical Astrophysics
Fundersnot available
KeywordsTransverse planePolarization (electrochemistry)Bent molecular geometryToroidMagnetic fieldActive galactic nucleusJet (fluid)Astrophysical jet

Abstract

fetched live from OpenAlex

Polarization has proved an invaluable tool for probing magnetic fields in relativistic jets. Maps of the intrinsic polarization vectors have provided the best evidence to date for uniform, toroidally dominated magnetic fields within jets. More recently, maps of the rotation measure (RM) in jets have for the first time probed the field geometry of the cool, moderately relativistic surrounding material. In most cases, clear signatures of toroidal magnetic field are detected, corresponding to gradients in RM profiles transverse to the jet. However, in many objects these profiles also display marked asymmetries which are difficult to explain in simple helical jet models. Furthermore, in some cases the RM profiles are strongly frequency and/or time dependent. Here we show that these features may be naturally accounted for by including relativistic helical motion in the jet model. In particular, we are able to reproduce bent RM profiles observed in a variety of jets, frequency dependent RM profile morphologies and even the time dependence of the RM profiles of knots in 3C 273. Finally, we predict that some sources may show reversals in their RM profiles at sufficiently high frequencies, depending upon the the ratio of the components of jet sheath velocity transverse and parallel to the jet. Thus, multi-frequency RM maps promise a novel way in which to probe the velocity structure of relativistic outflows.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.590
Threshold uncertainty score0.318

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.014
GPT teacher head0.240
Teacher spread0.226 · 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 designTheoretical or conceptual
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

Citations38
Published2009
Admission routes1
Has abstractyes

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