The 100 pc White Dwarf Sample in the SDSS Footprint
Bibliographic record
Abstract
Abstract We present follow-up spectroscopy of 711 white dwarfs within 100 pc, and we present a detailed model atmosphere analysis of the 100 pc white dwarf sample in the Sloan Digital Sky Survey footprint. Our spectroscopic follow-up is complete for 83% of the white dwarfs hotter than 6000 K, where the atmospheric composition can be constrained reliably. We identify 1508 DA white dwarfs with pure hydrogen atmospheres. The DA mass distribution has an extremely narrow peak at 0.59 M ⊙ and reveals a shoulder from relatively massive white dwarfs with M = 0.7–0.9 M ⊙. Comparing this distribution with binary population synthesis models, we find that the contribution from single stars that form through mergers cannot explain the overabundance of massive white dwarfs. In addition, the mass distribution of cool DAs shows a near absence of M > 1 M ⊙ white dwarfs. The pile-up of 0.7–0.9 M ⊙ and the disappearance of M > 1 M ⊙ white dwarfs is consistent with the effects of core crystallization. Even though the evolutionary models predict the location of the pile-up correctly, the delay from the latent heat of crystallization by itself is insufficient to create a significant pile-up, and additional cooling delays from related effects like phase separation are necessary. We also discuss the population of infrared-faint (ultracool) white dwarfs and demonstrate for the first time the existence of a well-defined sequence in color and magnitude. Curiously, this sequence is connected to a region in the color–magnitude diagrams where the number of white dwarfs with a helium-dominated atmosphere is low. This suggests that the infrared-faint white dwarfs likely have mixed H/He atmospheres.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".