Analysis of Helium-rich White Dwarfs Polluted by Heavy Elements in the Gaia Era
Bibliographic record
Abstract
Abstract We present an analysis of 1023 DBZ/DZ(A) and 319 DQ white dwarf stars taken from the Montreal White Dwarf Database. This represents a significant increase over the previous comprehensive studies on these types of objects. We use new trigonometric parallax measurements from the Gaia second data release, together with photometry from the Sloan Digital Sky Survey, Pan-STARRS, Gaia , or BVRI from the literature, which allow the determination of the mass for the majority of the objects in our sample. We use the photometric and spectroscopic techniques with our recently improved model atmospheres code, which include high-density effects, to accurately determine the effective temperature, surface gravity, and heavy-element abundances for each object. We study the abundance of hydrogen in DBZ/DZ white dwarfs and the properties of the accreted planetesimals. We explore the nature of the second sequence of DQ stars using proper motions from Gaia and highlight evidence of crystallization in massive DQ stars. We also present mass distributions for both spectral types. Finally, we discuss the implications of our findings in the context of the spectral evolution of white dwarfs and provide the atmospheric parameters for each star.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".