TOWARD A SPECTROSCOPIC CENSUS OF WHITE DWARFS WITHIN 40 pc OF THE SUN
Bibliographic record
Abstract
We present the preliminary results of a survey aimed at significantly increasing the range and completeness of the local census of spectroscopically confirmed white dwarfs. The current census of nearby white dwarfs is reasonably complete only to about 20 pc of the Sun, a volume that includes around 130 white dwarfs, a sample too small for detailed statistical analyses. This census is largely based on follow-up investigations of stars with very large proper motions. We describe here the basis of a method that will lead to a catalog of white dwarfs within 40 pc of the Sun and north of the celestial equator, thus increasing by a factor of eight the extent of the northern sky census. White dwarf candidates are identified from the SUPERBLINK proper motion database, allowing us to investigate stars down to a proper motion limit μ > 40 mas yr −1 , while minimizing the kinematic bias for nearby objects. The selection criteria and distance estimates are based on a combination of color–magnitude and reduced proper motion diagrams. Our follow-up spectroscopic observation campaign has so far uncovered 193 new white dwarfs, among which we identify 127 DA (including 9 DA+dM and 4 magnetic), 1 DB, 56 DC, 3 DQ, and 6 DZ stars. We perform a spectroscopic analysis on a subsample of 84 DAs, and provide their atmospheric parameters. In particular, we identify 11 new white dwarfs with spectroscopic distances within 25 pc of the Sun, including five candidates to the D < 20 pc subset.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".