On the Measurement of Fundamental Parameters of White Dwarfs in the Gaia Era
Bibliographic record
Abstract
Abstract We present a critical review of the determination of fundamental parameters of white dwarfs discovered by the Gaia mission. We first reinterpret color–magnitude and color–color diagrams using photometric and spectroscopic information contained in the Montreal White Dwarf Database (MWDD), combined with synthetic magnitudes calculated from a self-consistent set of model atmospheres with various atmospheric compositions. The same models are then applied to measure the fundamental parameters of white dwarfs using the so-called photometric technique, which relies on the exquisite Gaia trigonometric parallax measurements, and photometric data from Panoramic Survey Telescope And Rapid Response System, Sloan Digital Sky Survey, and Gaia. In particular, we discuss at length the systematic effects induced by these various photometric systems. We then study in great detail the mass distribution as a function of effective temperature for the white dwarfs spectroscopically identified in the MWDD, as well as for the white dwarf candidates discovered by Gaia. We pay particular attention to the assumed atmospheric chemical composition of cool, non-DA stars. We also briefly revisit the validity of the mass–radius relation for white dwarfs and the recent discovery of the signature of crystallization in the Gaia color–magnitude diagram for DA white dwarfs. We finally present evidence that the core composition of most of these white dwarfs is, in bulk, a mixture of carbon and oxygen, an expected result from stellar evolution theory, but never empirically well established before.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".