The GALEX-Gaia-EDR3 Catalogue of Single and Binary White Dwarfs
Bibliographic record
Abstract
We present a catalogue of white dwarf candidates constructed from the GALEX and Gaia EDR3 catalogues. The catalogue contains 332,111 candidate binary white dwarf systems and 111,996 candidate single white dwarfs. Where available, the catalogue is augmented with photometry from Pan-STARRS DR1, SDSS DR12 and classifications from StarHorse. We fit photometric data with modeled white dwarf cooling sequences to derive mass, age and effective temperature of the white dwarf as well as mass estimates for the companion. We test our classifications against StarHorse, the Gentile-Fusillo Gaia EDR3 catalogue, and white-dwarf-main-sequence binaries identified in SDSS DR12. This catalogue provides a unique probe of the binarity of white dwarfs as well as the abundance of white-dwarf giant binaries and large mass-ratio stellar binaries which are difficult to probe otherwise.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.012 |
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".