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Record W4220722557 · doi:10.1093/mnras/stac701

MeerKAT uncovers the physics of an odd radio circle

2022· article· en· W4220722557 on OpenAlexaff
R. P. Norris, J. D. Collier, Roland M. Crocker, Ian Heywood, Peter J. Macgregor, L. Rudnick, Stanislav S. Shabala, H. Andernach, Elisabete da Cunha, Jayanne English, M. D. Filipović, B. Koribalski, Kieran Luken, A. S. G. Robotham, Srikrishna Sekhar, Jessica E Thorne, Tessa Vernstrom

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadio Astronomy Observations and Technology
Canadian institutionsUniversity of Manitoba
FundersAustralian Research CouncilScience and Technology Facilities CouncilJet Propulsion LaboratoryNational Research FoundationNational Aeronautics and Space AdministrationCalifornia Institute of TechnologyUniversity of MinnesotaUniversity of CaliforniaNational Science Foundation
KeywordsPhysicsPathfinderSpectral indexRedshiftAstrophysicsRadio telescopeRadio astronomyGalaxyAstronomyPolarization (electrochemistry)Radio galaxySensitivity (control systems)Spectral lineLibrary science

Abstract

fetched live from OpenAlex

ABSTRACT Odd radio circles (ORCs) are recently-discovered faint diffuse circles of radio emission, of unknown cause, surrounding galaxies at moderate redshift (z ∼ 0.2 – 0.6). Here, we present detailed new MeerKAT radio images at 1284 MHz of the first ORC, originally discovered with the Australian Square Kilometre Array Pathfinder, with higher resolution (6 arcsec) and sensitivity (∼ 2.4 μJy/beam). In addition to the new images, which reveal a complex internal structure consisting of multiple arcs, we also present polarization and spectral index maps. Based on these new data, we consider potential mechanisms that may generate the ORCs.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.196
Teacher spread0.189 · 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

Citations50
Published2022
Admission routes1
Has abstractyes

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