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An Australia Telescope Compact Array 20-cm radio continuum study of the Large Magellanic Cloud

2007· article· en· W2170797983 on OpenAlexaff
Annie Hughes, L. Staveley‐Smith, Sungeun Kim, M. Wolleben, M. D. Filipović

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadio Astronomy Observations and Technology
Canadian institutionsNational Research Council CanadaUniversity of AlbertaHerzberg Institute of Astrophysics
Fundersnot available
KeywordsPhysicsLarge Magellanic CloudAstrophysicsSpectral indexAstronomyTelescopeRadio telescopeSupernova remnantSynchrotron radiationSupernovaGalaxySpectral lineOptics

Abstract

fetched live from OpenAlex

We present a mosaic image of the 1.4-GHz radio continuum emission from the Large Magellanic Cloud (LMC) observed with the Australia Telescope Compact Array (ATCA) and the Parkes Telescope. The mosaic covers with an angular resolution of 40 arcsec, corresponding to a spatial scale of ∼10 pc in the LMC. The final image is suitable for studying emission on all scales between 40 arcsec and the surveyed area. In this paper, we discuss (i) the characteristics of the LMC's diffuse and compact radio continuum emission, (ii) the fraction of the emission produced by thermal processes and the implied star formation rate in the LMC and (iii) variations in the radio spectral index across the LMC. Two non-standard reduction techniques that we used to process the ATCA visibility data may be of interest for future wide-field radio continuum surveys. The data are open to the astronomical community and should be a rich resource for studies of individual objects such as supernova remnants, H ii regions and planetary nebulae as well as extended features such as the diffuse emission from synchrotron radiation.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.023

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 communication0.0000.000
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 head0.012
GPT teacher head0.245
Teacher spread0.233 · 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

Citations95
Published2007
Admission routes1
Has abstractyes

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