On the Nature of Ultracool White Dwarfs: Not so Cool after All
Bibliographic record
Abstract
Abstract A recent analysis of the 100 pc white dwarf sample in the SDSS footprint demonstrated for the first time the existence of a well-defined ultracool—or IR-faint—white dwarf sequence in the Hertzsprung–Russell diagram. Here we take advantage of this discovery to enlarge the IR-faint white dwarf sample threefold. We expand our selection to the entire Pan-STARRS survey footprint as well as the Montreal White Dwarf Database 100 pc sample and identify 37 candidates with strong flux deficits in the optical. We present follow-up Gemini optical spectroscopy of 30 of these systems and confirm all of them as IR-faint white dwarfs. We identify an additional set of 33 objects as candidates based on their colors and magnitudes. We present a detailed model atmosphere analysis of all 70 newly identified IR-faint white dwarfs together with 35 previously known objects reported in the literature. We discuss the physics of model atmospheres and show that the key physical ingredient missing in our previous generation of model atmospheres was the high-density correction to the He− free–free absorption coefficient. With new model atmospheres calculated for the purpose of this analysis, we now obtain significantly higher effective temperatures and larger stellar masses for these IR-faint white dwarfs than the T eff and M values reported in previous analyses, thus solving a two-decade-old problem. In particular, we identify in our sample a group of ultramassive white dwarfs in the Debye cooling phase with stellar parameters never measured before.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".