VAST-MeMeS: Characterising non-thermal radio emission from magnetic massive stars using the Australian SKA Pathfinder
Bibliographic record
Abstract
Abstract Magnetic massive stars are stars of spectral types O, B, and A that harbour $\sim$ kG strength (mostly dipolar) surface magnetic fields. Their non-thermal radio emission has been demonstrated to be an important magnetospheric probe, provided the emission is fully characterised. A necessary step for that is to build a statistically significant sample of radio-bright magnetic massive stars. In this paper, we present the ‘VAST project to study Magnetic Massive Stars’ or VAST-MeMeS that aims to achieve that by taking advantage of survey data acquired with the Australian SKA Pathfinder telescope. VAST-MeMeS is defined under the ‘Variables and Slow Transients’ survey, although it also uses data from other ASKAP surveys. We found radio detections from 48 magnetic massive stars, out of which, 14 do not have any prior radio detections. We also identified 9 ‘Main-sequence Radio Pulse Emitter’ candidates based on variability and circular polarisation of flux densities. The expanded sample suggests a slightly lower efficiency in the radio production than that reported in earlier work. In addition to significantly expanding the sample of radio-bright magnetic massive stars, the addition of flux density measurements at ${\lesssim} 1$ GHz revealed that the spectra of incoherent radio emission can extend to much lower frequencies than that assumed in the past. In the future, radio observations spanning wide frequency and rotational phase ranges should be conducted so as to reduce the uncertainties in the incoherent radio luminosities. The results from these campaigns, supplemented with precise estimations of stellar parameters, will allow us to fully understand particle acceleration and non-thermal radio production in large-scale stellar magnetospheres.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".