A <i>GALEX</i> view of the DA white dwarf population
Bibliographic record
Abstract
ABSTRACT We present a detailed model atmosphere analysis of 14001 DA white dwarfs from the Montreal White Dwarf Database with ultraviolet photometry from the GALEX mission. We use the 100 pc sample, where the extinction is negligible, to demonstrate that there are no major systematic differences between the best-fitting parameters derived from optical only data and the optical + UV photometry. GALEX FUV and NUV data improve the statistical errors in the model fits, especially for the hotter white dwarfs with spectral energy distributions that peak in the UV. Fitting the UV to optical spectral energy distributions also reveals UV-excess or UV-deficit objects. We use two different methods to identify outliers in our model fits. Known outliers include objects with unusual atmospheric compositions, strongly magnetic white dwarfs, and binary white dwarfs, including double degenerates and white dwarf + main-sequence systems. We present a list of 89 newly identified outliers based on GALEX UV data; follow-up observations of these objects will be required to constrain their nature. Several current and upcoming large-scale spectroscopic surveys are targeting >105 white dwarfs. In addition, the ULTRASAT mission is planning an all-sky survey in the NUV band. A combination of the UV data from GALEX and ULTRASAT and optical data on these large samples of spectroscopically confirmed DA white dwarfs will provide an excellent opportunity to identify unusual white dwarfs in the solar neighbourhood.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".