The stellar population in the SARAO MeerKAT Galactic Plane Survey
Bibliographic record
Abstract
ABSTRACT We report on optically selected stellar candidates of SARAO MeerKAT 1.3 GHz radio continuum survey sources of the Galactic plane. Stellar counterparts to radio sources are selected by cross-matching the MeerKAT source positions with Gaia DR3, using two approaches. The first approach evaluated the probability of chance alignments between the radio survey and Gaia sources and used AllWISE infrared colour–colour information to select potential stellar candidates. The second approach utilized a Monte Carlo method to evaluate the cross-matching reliability probability, based on populations of known radio-emitting stars. From the combined approaches, we found 629 potential stellar counterparts, of which 169 have existing SIMBAD classifications, making it the largest Galactic plane radio-optical cross-match sample to date. A colour–magnitude analysis of the sample revealed a diverse population of stellar objects, ranging from massive OB stars, main-sequence stars, giants, young stellar objects, emission line stars, red dwarfs, and white dwarfs. Some of the proposed optical counterparts include chromospherically/coronally active stars, for example RS CVn binaries, BY Dra systems, YSOs, and flare stars, which typically exhibit radio emission. Based on Gaia’s low-resolution spectroscopy, some of the stars show strong H$\alpha$ emission, indicating they are magnetically active, consistent with them being radio emitters. While MeerKAT’s sensitivity and survey speed make it ideal for detecting faint radio sources, its angular resolution limits accurate counterpart identification for crowded fields such as the Galactic Plane. Higher frequency, and, thereby, better spatial resolution, radio observations plus circular polarization would be required to strengthen the associations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".