New BRITE-Constellation observations of the roAp star <i>α</i> Cir
Bibliographic record
Abstract
Context. Chemically peculiar (CP) stars with a measurable magnetic field comprise the group of mCP stars. The pulsating members define the subgroup of rapidly oscillating Ap (roAp) stars, of which α Cir is the brightest member. Hence, α Cir allows the application of challenging techniques, such as interferometry, very high temporal and spectral resolution photometry, and spectroscopy in a wide wavelength range, that have the potential to provide unique information about the structure and evolution of a star. Aims. Based on new photometry from BRITE-Constellation, obtained with blue and red filters, and on photometry from WIRE, SMEI, and TESS we attempt to determine the surface spot structure of α Cir and investigate pulsation frequencies. Methods. We used photometric surface imaging and frequency analyses and Bayesian techniques in order to quantitatively compare the probability of different models. Results. BRITE-Constellation photometry obtained from 2014 to 2016 is put in the context of space photometry obtained by WIRE, SMEI, and TESS. This provides improvements in the determination of the rotation period and surface features (three spots detected and a fourth one indicated). The main pulsation frequencies indicate two consecutive radial modes and one intermediate dipolar mode. Advantages and problems of the applied Bayesian technique are discussed.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".