A Comprehensive Spectroscopic Analysis of DB White Dwarfs
Bibliographic record
Abstract
We present a detailed analysis of 108 helium-line (DB) white dwarfs based on model atmosphere fits to high signal-to-noise optical spectroscopy.We derive a mean mass of 0.67 M ⊙ for our sample, with a dispersion of only 0.09 M ⊙ .White dwarfs also showing hydrogen lines, the DBA stars, comprise 44% of our sample, and their mass distribution appears similar to that of DB stars.As in our previous investigation, we find no evidence for the existence of low-mass (M < 0.5 M ⊙ ) DB white dwarfs.We derive a luminosity function based on a subset of DB white dwarfs identified in the Palomar-Green survey.We show that 20% of all white dwarfs in the temperature range of interest are DB stars, although the fraction drops to half this value above T eff ∼ 20, 000 K. We also show that the persistence of DB stars with no hydrogen features at low temperatures is difficult to reconcile with a scenario involving accretion from the interstellar medium, often invoked to account for the observed hydrogen abundances in DBA stars.We present evidence for the existence of two different evolutionary channels that produce
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".