A benchmark rapidly oscillating chemically peculiar (roAp) star:
Bibliographic record
Abstract
Context. The brightest chemically peculiar magnetic (mCP) star, α Cir, is also pulsating. Precise photometric and spectroscopic data, preferably with a long time base, are needed to investigate its evolutionary aspects as well. The present investigation of α Cir offers a space-based high-precision photometry study with high time resolution, covering 20+ years and supplemented by high resolution spectroscopy from the ground. Aims. We discuss the controversial rotation periods that have been recently reported and we consider new determinations of the actual values. We process the complex pulsation frequency spectrum, considering the implications in modelling the structure of α Cir. Methods. We developed an automated Bayesian algorithm to consistently search for periodic signals in the WIRE, SMEI, TESS, and BRITE space photometric datasets, complemented by radial velocity data from HARPS. Results. New observations in 2021 and 2023 from TESS and BRITE indicate a detection of α Cir as a triple system. The rotation period of α CirA has been determined as 4.4792890 ± 0.0000018 d. The TESS data show a rich frequency spectrum including three l = 0, six l = 1, two l = 2, and one l = 3 modes. Of these, five are shown to be rotationally split. The dipole modes show significant curvature in the echelle diagram, probably due to the strong magnetic field of α Cir. Conclusions. Overall, α Cir continues to be a cornerstone of mCP stars. A confirmation of the triple system requires additional space photometry and/or high-resolution spectroscopy to increase the time base. These data are also needed to improve the quality of the pulsation frequency spectrum and to investigate the evolutionary effects at play. A detailed seismic modelling study that considers the effects of a magnetic field on pulsation is subsequently recommended.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".