Identifying and Characterizing New Nearby White Dwarfs
Bibliographic record
Abstract
How confident are we that all of the nearest white dwarfs (WDs) have been identified? In an effort to answer this question, we have begun an initiative to identify and characterize new nearby WDs, particularly in the southern hemisphere. We estimate physical parameters for new WDs using medium resolution (R ~1000) optical spectroscopy, and distances using optical photometry combined with 2MASS near-infrared photometry. For objects within 25 pc (Catalogue of Nearby Stars, and NStars Database horizons), we determine a trigonometric parallax via CTIOPI (Cerro Tololo Inter-American Observatory Parallax Investigation). Of the 37 new WD systems discovered so far, fourteen are likely within 25 pc, a volume that contains 107 WDs with trigonometric parallaxes. Interesting objects include two that are likely double degenerates including one with a magnetic component, one that is a cool (T$_{eff}$ ~5000 K) likely mixed atmosphere WD with deficient flux at near-infrared wavelengths, and two that are metal-rich. Observations are underway via the Hubble Space Telescope to resolve four potential double degenerates (the new magnetic WD and three other previously known WDs) for dynamical mass determinations. All ground-based observations are obtained as part of the SMARTS (Small and Moderate Aperture Research Telescope System) Consortium at CTIO.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".