APASS LANDOLT-SLOAN<i>BVgri</i>PHOTOMETRY OF RAVE STARS. I. DATA, EFFECTIVE TEMPERATURES, AND REDDENINGS
Bibliographic record
Abstract
We provide AAVSO Photometric All-Sky Survey (APASS) photometry in the Landolt BV and Sloan g ' r ' i ' bands for all 425,743 stars included in the fourth RAVE Data Release. The internal accuracy of the APASS photometry of RAVE stars, expressed as the error of the mean of data obtained and separately calibrated over a median of four distinct observing epochs and distributed between 2009 and 2013, is 0.013, 0.012, 0.012, 0.014, and 0.021 mag for the B , V , g ', r ', and i ' bands, respectively. The equally high external accuracy of APASS photometry has been verified on secondary Landolt and Sloan photometric standard stars not involved in the APASS calibration process and on a large body of literature data on field and cluster stars, confirming the absence of offsets and trends. Compared with the Carlsberg Meridian Catalog (CMC-15), APASS astrometry of RAVE stars is accurate to a median value of 0.098 arcsec. Brightness distribution functions for the RAVE stars have been derived in all bands. APASS photometry of RAVE stars, augmented by 2MASS JHK infrared data, has been χ 2 fitted to a densely populated synthetic photometric library designed to widely explore temperature, surface gravity, metallicity, and reddening. Resulting T eff and E B − V , computed over a range of options, are provided and discussed, and will be kept updated in response to future APASS and RAVE data releases. In the process, we find that the reddening caused by a homogeneous slab of dust, extending for 140 pc on either side of the Galactic plane and responsible for = 0.036 ± 0.002 at the Galactic poles, is a suitable approximation of the actual reddening encountered at Galactic latitudes | b | ⩾ 25°.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 0.034 |
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".