A SPECTROSCOPIC SURVEY AND ANALYSIS OF BRIGHT, HYDROGEN-RICH WHITE DWARFS
Bibliographic record
Abstract
We have conducted a spectroscopic survey of over 1300 bright ( V ⩽ 17.5), hydrogen-rich white dwarfs based largely on the last published version of the McCook & Sion catalog. The complete results from our survey, including the spectroscopic analysis of over 1100 DA white dwarfs, are presented. High signal-to-noise ratio optical spectra were obtained for each star and were subsequently analyzed using our standard spectroscopic technique where the observed Balmer line profiles are compared to synthetic spectra computed from the latest generation of model atmospheres appropriate for these stars. First, we present the spectroscopic content of our sample, which includes many misclassifications as well as several DAB, DAZ, and magnetic white dwarfs. Next, we look at how the new Stark broadening profiles affect the determination of the atmospheric parameters. When necessary, specific models and analysis techniques are used to derive the most accurate atmospheric parameters possible. In particular, we employ M dwarf templates to obtain better estimates of the atmospheric parameters for those white dwarfs that are in DA+dM binary systems. Certain unique white dwarfs and double-degenerate binary systems are also analyzed in greater detail. We then examine the global properties of our sample including the mass distribution and their distribution as a function of temperature. We then proceed to test the accuracy and robustness of our method by comparing our results to those of other surveys such as SPY and Sloan Digital Sky Survey. Finally, we revisit the ZZ Ceti instability strip and examine how the determination of its empirical boundaries is affected by the latest line profile calculations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".