Synthetic Stellar Photometry – II. Testing the bolometric flux scale and tables of bolometric corrections for the Hipparcos/Tycho, Pan-STARRS1, SkyMapper, and JWST systems
Bibliographic record
Abstract
We useMARCSmodel atmosphere fluxes to compute synthetic colours, bolometric corrections and reddening coefficients for the Hipparcos/Tycho, Pan-STARRS1, SkyMapper, and JWST systems. Tables and interpolation subroutines are provided to transform isochrones from the theoretical to various observational planes, to derive bolometric corrections, synthetic colours and colour-temperature relations at nearly any given point of the HR diagram for 2600K ≤ Teff ≤ 8000 K, and different values of reddening in 85 photometric filters. We use absolute spectrophotometry from the CALSPEC library to show that bolometric fluxes can be recovered to ~2 per cent from bolometric corrections in a single band, when input stellar parameters are well known for FG dwarfs at various metallicities. This sole source of uncertainty impacts interferometric Teff to ≃0.5 per cent (or 30 K at the solar temperature). Uncertainties are halved when combining bolometric corrections in more bands, and limited by the fundamental uncertainty of the current absolute flux scale at 1 per cent. Stars in the RAVE DR5 catalogue are used to validate the quality of our MARCS synthetic photometry in selected filters across the optical and infrared range. This investigation shows that extant MARCSsynthetic fluxes are able to reproduce the main features observed in stellar populations across the Galactic disc.
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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 | 0.000 | 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) | 0.003 | 0.002 |
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".