COMPARISON OF ATMOSPHERIC PARAMETERS DETERMINED FROM SPECTROSCOPY AND PHOTOMETRY FOR DA WHITE DWARFS IN THE SLOAN DIGITAL SKY SURVEY
Bibliographic record
Abstract
We present a comparative analysis of atmospheric parameters obtained with the so-called photometric and spectroscopic techniques. Photometric and spectroscopic data for 1360 DA white dwarfs from the Sloan Digital Sky Survey (SDSS) are used, as well as spectroscopic data from the Villanova White Dwarf Catalog. We first test the calibration of the ugriz photometric system by using model atmosphere fits to observed data. Our photometric analysis indicates that the ugriz photometry appears well calibrated when the SDSS to AB 95 zeropoint corrections are applied. The spectroscopic analysis of the same data set reveals that the so-called high-log g problem can be solved by applying published correction functions that take into account three-dimensional hydrodynamical effects. However, a comparison between the SDSS and the White Dwarf Catalog spectra also suggests that the SDSS spectra still suffer from a small calibration problem. We then compare the atmospheric parameters obtained from both fitting techniques and show that the photometric temperatures are systematically lower than those obtained from spectroscopic data. This systematic offset may be linked to the hydrogen line profiles used in the model atmospheres. We finally present the results of an analysis aimed at measuring surface gravities using photometric data only.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".