A Quantitative Analysis of the Available Multicolor Photometry for Rapidly Pulsating Hot B Subdwarfs
Bibliographic record
Abstract
We present a quantitative and homogeneous analysis of the broadband multicolor photometric data sets gathered so far on rapidly pulsating hot B subdwarf stars. This concerns seven distinct data sets related to six different stars. Our analysis is carried out within the theoretical framework developed by Randall et al., which includes full nonadiabatic effects. The goal of this analysis is partial mode identification, i.e., the determination of the degree index l of each of the observed pulsation modes. We assume possible values of l from 0 to 5 in our calculations. For each target star, we compute a specific model atmosphere and a specific pulsation model using estimates of the atmospheric parameters coming from time-averaged optical spectroscopy. For every assumed value of l , we use a formal χ 2 approach to model the observed amplitude-wavelength distribution of each mode, and we compute a quality of fit Q probability to quantify the derived fit and to discriminate objectively between the various solutions. We find that no completely convincing and unambiguous l identification is possible on the basis of the available data, although partial mode discrimination has been reached for 25 out of the 41 modes studied. A brief statistical study of these results suggests that a majority of the modes must have l -values of 0, 1, and 2, but also that modes with l = 4 could very well be present, while modes with l = 3 appear to be rarer. This is in line with recent results showing that l = 4 modes in rapidly pulsating B subdwarfs have a higher visibility in the optical domain than modes with l = 3. Although somewhat disappointing in terms of mode discrimination, our results still suggest that the full potential of multicolor photometry for l identification in pulsating subdwarfs is within reach. It will be a matter of gathering higher S/N ratio observations than has been done up to now.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".