Physical Properties of the Current Census of Northern White Dwarfs within 40 pc of the Sun
Bibliographic record
Abstract
We present a detailed description of the physical properties of our current census of white dwarfs within 40 pc of the Sun, based on an exhaustive spectroscopic survey of northern hemisphere candidates from the SUPERBLINK proper motion database. Our method for selecting white dwarf candidates is based on a combination of theoretical color-magnitude relations and reduced proper motion diagrams. We reported in an earlier publication the discovery of nearly 200 new white dwarfs, and we present here the discovery of an additional 133 new white dwarfs, among which we identify 96 DA, 3 DB, 24 DC, 3 DQ, and 7 DZ stars. We further identify 178 white dwarfs that lie within 40 pc of the Sun, representing a 40% increase of the current census, which now includes 492 objects. We estimate the completeness of our survey at between 66 and 78%, allowing for uncertainties in the distance estimates. We also perform a homogeneous model atmosphere analysis of this 40 pc sample and find a large fraction of massive white dwarfs, indicating that we are successfully recovering the more massive, and less luminous objects often missed in other surveys. We also show that the 40 pc sample is dominated by cool and old white dwarfs, which populate the faint end of the luminosity function, although trigonometric parallaxes will be needed to shape this part of the luminosity function more accurately. Finally, we identify 4 probable members of the 20 pc sample, 4 suspected double degenerate binaries, and we also report the discovery of two new ZZ Ceti pulsators.
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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 | 0.002 |
| 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 | 0.001 |
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".