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Record W2762122741 · doi:10.3390/galaxies5040066

Radio Polarisation Study of High Rotation Measure AGNs

2017· article· en· W2762122741 on OpenAlexaff
Yik Ki, Sui Ann Mao, Aritra Basu, Carl Heiles, Jennifer West

Bibliographic record

VenueGalaxies · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
FundersInternational Max Planck Research School for Advanced Methods in Process and Systems EngineeringRheinische Friedrich-Wilhelms-Universität BonnInternational Max Planck Research School for Environmental, Cellular and Molecular MicrobiologyNational Science Foundation
KeywordsPhysicsAstrophysicsMeasure (data warehouse)Rotation (mathematics)TraversePolarimetryActive galactic nucleusAmbiguityGalaxyGeodesyGeometryOpticsGeologyScatteringMathematicsComputer science

Abstract

fetched live from OpenAlex

As radio polarised emission from astrophysical objects traverse through foreground magnetised plasma, the physical conditions along the lines of sight are encrypted in the form of rotation measure (RM). We performed broadband spectro-polarimetric observations of high rotation measure ( | RM | ≳ 300 rad m − 2 ) sources away from the Galactic plane ( | b | > 10 ∘ ) selected from the NVSS RM catalogue. The main goals are to verify the NVSS RM values, which could be susceptible to n π -ambiguity, as well as to identify the origin of the extreme RM values. We show that 40 % of our sample suffer from n π -ambiguity in the NVSS RM catalogue. There are also hints of RM variabilities over ∼20 years epoch for most of our sources, as revealed by comparing the RM values of the two studies in the same frequency ranges after correcting for n π -ambiguity. At last, we demonstrate the possibility of applying QU-fitting to study the ambient media of AGNs.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.014
GPT teacher head0.235
Teacher spread0.221 · 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 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
Published2017
Admission routes1
Has abstractyes

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