SIGNATURES OF RELATIVISTIC HELICAL MOTION IN THE ROTATION MEASURES OF ACTIVE GALACTIC NUCLEUS JETS
Bibliographic record
Abstract
Polarization has proven to be 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 the 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 that 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 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".