AN IMPROVED SPECTROSCOPIC ANALYSIS OF DA WHITE DWARFS FROM THE SLOAN DIGITAL SKY SURVEY DATA RELEASE 4
Bibliographic record
Abstract
We present an improved spectroscopic and photometric analysis of hydrogen-line DA white dwarfs from the Sloan Digital Sky Survey Data Release 4 (SDSS DR4) based on model atmospheres that include improved Stark broadening profiles with non-ideal gas effects. We also perform a careful visual inspection of all spectroscopic fits with high signal-to-noise ratios (S/Ns > 12) and present improved atmospheric parameters (Teff and log g) for each white dwarf. Through a comparison of spectroscopic and photometric temperatures, we report the discovery of 35 DA+DB/DC double degenerate candidates and two helium-rich DA stars. We also determine that a cutoff at S/N = 15 optimizes the size and quality of the sample for computing the mean mass of DA white dwarfs, for which we report a value of 0.613 M☉. We compare our results to previous analyses of the SDSS DR4 and find a good agreement if we account for the shift produced by the improved Stark profiles. Finally, the properties of DA white dwarfs in the SDSS are weighed against those of the Villanova White Dwarf Catalog sample of Gianninas et al. We find systematically lower masses (by about 3% on average), a difference that we trace back to the data reduction procedure of the SDSS. We conclude that a better understanding of these differences will be important to determine the absolute temperature scale and mean mass of DA white dwarfs.
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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 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".