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Record W6910773763 · doi:10.48550/arxiv.2204.08861

The MAVERIC survey: A catalogue of radio sources in southern globular clusters from the Australia Telescope Compact Array

2022· preprint· en· W6910773763 on OpenAlexaff

Bibliographic record

VenuearXiv (Cornell University) · 2022
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGlobular clusterTelescopeRadio telescopeDemographicsPopulationCluster (spacecraft)

Abstract

fetched live from OpenAlex

Radio continuum observations offer a new window on compact objects in globular clusters compared to typical X-ray or optical studies. As part of the MAVERIC survey, we have used the Australia Telescope Compact Array to carry out a deep (median central noise level of approximately 4 microJy per beam) radio continuum survey of 26 southern globular clusters at central frequencies of 5.5 and 9.0 GHz. This paper presents a catalogue of 1285 radio continuum sources in the fields of these 26 clusters. Considering the surface density of background sources, we find significant evidence for a population of radio sources in seven of the 26 clusters, and also identify at least 11 previously known compact objects (6 pulsars and 5 X-ray binaries). While the overall density of radio continuum sources with 7.25-GHz flux densities greater than about 20 microJy in typical globular clusters is relatively low, the survey has already led to the discovery of several exciting compact binaries, including a candidate ultracompact black hole X-ray binary in 47 Tuc. Many of the unclassified radio sources near the centres of the clusters are likely to be true cluster sources, and multi-wavelength follow-up will be necessary to classify these objects and better understand the demographics of accreting compact binaries in globular clusters.

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 score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.070
GPT teacher head0.202
Teacher spread0.132 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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