Recipes for bolometric corrections and <i>Gaia</i> luminosities of B-type stars: application to an asteroseismic sample
Bibliographic record
Abstract
ABSTRACT We provide three statistical model prescriptions for the bolometric corrections appropriate for B-type stars as a function of (i) Teff, (ii) Teff and log g, and (iii)Teff, log g and [M/H]. These statistical models have been calculated for 27 different filters, including those of the Gaia space mission, and were derived based on two different grids of bolometric corrections assuming LTE and LTE+NLTE, respectively. Previous such work has mainly been limited to a single photometric passband without taking into account non-local thermodynamic equilibrium (NLTE) effects on the bolometric corrections. Using these statistical models, we calculate the luminosities of 34 slowly pulsating B-type (SPB) stars with available spectroscopic parameters, to place them in the Hertzsprung–Russell diagram and to compare their position to the theoretical SPB instability strip. We find that excluding NLTE effects has no significant effect on the derived luminosities for the temperature range 11 500–21 000 K. We conclude that spectroscopic parameters are needed in order to achieve meaningful luminosities of B-type stars. The three prescriptions for the bolometric corrections are valid for any galactic B-type star with effective temperatures and surface gravities in the ranges 10 000–30 000 K and 2.5–4.5 dex, respectively, covering regimes below the Eddington limit.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".