The First Radio Spectrum of a Rapidly Rotating A-type Star
Bibliographic record
Abstract
Abstract The radio spectra of main-sequence stars remain largely unconstrained due to the lack of observational data to inform stellar atmosphere models. As such, the dominant emission mechanisms at long wavelengths, how they vary with spectral type, and how much they contribute to the expected brightness at a given radio wavelength are still relatively unknown for most spectral types. We present radio continuum observations of Altair, a rapidly rotating A-type star. We observed Altair with NOEMA in 2018 and 2019 at 1.34, 2.09, and 3.22 mm and with the Very Large Array in 2019 at 6.7 and 9.1 mm. In the radio spectra, we see a brightness temperature minimum at millimeter wavelengths followed by a steep rise to temperatures larger than the optical photosphere, behavior that is unexpected for A-type stars. We use these data to produce the first submillimeter to centimeter spectrum of a rapidly rotating A-type star informed by observations. We generated both PHOENIX and KINICH-PAKAL model atmospheres and determine the KINICH-PAKAL model better reproduces Altair’s radio spectrum. The synthetic spectrum shows a millimeter brightness temperature minimum followed by significant emission over that of the photosphere at centimeter wavelengths. Together, these data and models show how the radio spectrum of an A-type star can reveal the presence of a chromosphere, likely induced by rapid rotation, and that a Rayleigh Jean’s extrapolation of the stellar photosphere is not an adequate representation of a star’s radio spectrum.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".