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Record W2995050199 · doi:10.1017/s1743921320003014

Understanding the origin of radio outflows in Seyfert galaxies using radio polarimetry

2019· preprint· en· W2995050199 on OpenAlexafffund
Biny Sebastian, Preeti Kharb, Christopher P. O’ Dea, J. F. Gallimore, S. A. Baum, E. J. M. Colbert

Bibliographic record

VenueProceedings of the International Astronomical Union · 2019
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsAstrophysicsRadio galaxyGalaxyActive galactic nucleusLuminous infrared galaxyAstronomyPolarimetryX-shaped radio galaxyScattering

Abstract

fetched live from OpenAlex

Abstract The role of starburst winds versus active galactic nuclei (AGN) jets/winds in the formation of the kiloparsec scale radio emission seen in Seyferts is not yet well understood. In order to be able to disentangle the role of various components, we have observed a sample of Seyfert galaxies exhibiting kpc-scale radio emission suggesting outflows, along with a comparison sample of starburst galaxies, with the EVLA B-array in polarimetric mode at 1.4 GHz and 5 GHz. The Seyfert galaxy NGC 2639, shows highly polarized secondary radio lobes, not observed before, which are aligned perpendicular to the known pair of radio lobes. The additional pair of lobes represent an older epoch of emission. A multi-epoch multi-frequency study of the starburst-Seyfert composite galaxy NGC 3079, reveals that the jet together with the starburst superwind and the galactic magnetic fields might be responsible for the well-known 8-shaped radio lobes observed in this galaxy. We find that many of the Seyfert galaxies in our sample show bubble-shaped lobes, which are absent in the starburst galaxies that do not host an AGN.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.250
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2019
Admission routes2
Has abstractyes

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