SPECTROSCOPIC ANALYSIS OF HOT, HYDROGEN-RICH WHITE DWARFS: THE PRESENCE OF METALS AND THE BALMER-LINE PROBLEM
Bibliographic record
Abstract
We present an analysis of optical spectra for 29 DAO white dwarfs. First, we present our new up-to-date model atmosphere grids computed without the assumption of local thermodynamic equilibrium in which we have included carbon, nitrogen, and oxygen at solar abundances. We demonstrate that the addition of these metals in the model atmospheres is essential in overcoming the Balmer-line problem, which manifests itself as an inability to fit all the Balmer lines simultaneously with consistent atmospheric parameters. We then present the spectroscopic analysis of our sample of DAO white dwarfs for which we determine the effective temperature, surface gravity, and helium abundance. We also present 18 hot DA white dwarfs that also suffer from the Balmer-line problem. We analyze these stars with models analogous to those for the DAO white dwarfs save for the presence of helium. Systematic differences between our newly determined atmospheric parameters with respect to previous determinations are explored. Far-ultraviolet spectra from the FUSE archive are then examined to demonstrate that there exists a correlation between higher metallic abundances and instances of the Balmer-line problem. The implications of these findings for all hot, hydrogen-rich white dwarfs are discussed. Specifically, the possible evolutionary scenario for DAO white dwarfs is revised and post-extreme horizontal branch evolution is no longer needed to explain the evolution for the majority of the DAO stars. Finally, we discuss how the presence of metals might drive a weak stellar wind which in turn could explain the presence of helium in DAO 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".