SPECTROSCOPIC ANALYSIS OF DA WHITE DWARFS: STARK BROADENING OF HYDROGEN LINES INCLUDING NONIDEAL EFFECTS
Bibliographic record
Abstract
We present improved calculations for the Stark broadening of hydrogen lines in dense plasmas typical of white dwarf atmospheres. Our new model is based on the unified theory of Stark broadening from Vidal, Cooper, and Smith. For the first time, we account for the nonideal effects in a consistent way directly inside the line profile calculations. The Hummer and Mihalas theory is used to describe the nonideal effects due to perturbations on the absorber from protons and electrons. We use a truncation of the electric microfield distribution in the quasi-static proton broadening to take into account the fact that high electric microfields dissociate the upper state of a transition. This approach represents a significant improvement over previous calculations that relied on the use of an ad hoc parameter to mimic these nonideal effects. We obtain the first model spectra with line profiles that are consistent with the equation of state. We revisit the properties of DA stars in the range 40,000 K > T eff > 13,000 K by analyzing the optical spectra with our improved models. The updated atmospheric parameters are shown to differ substantially from those published in previous studies, with a mean mass shifted by +0.034 M ☉ . We also show that these revised atmospheric parameters yield absolute visual magnitudes that remain in excellent agreement with trigonometric parallax measurements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".