The cooling of massive white dwarfs from <i>Gaia</i> EDR3
Bibliographic record
Abstract
ABSTRACT We determine the distribution of cooling ages of massive Gaia EDR3 white dwarfs identified with over 90 per cent probability within 200 pc and with mass in the range 0.95–1.25 M⊙. Using three sets of publicly available models, we consider sub-samples of these white dwarfs sorted into three equally spaced mass bins. Under the assumption of a constant white dwarf formation rate, we find an excess of white dwarfs, both along the Q branch and below it, corresponding respectively to stars that are in the process of freezing and those that are completely frozen. We compare the cooling age distributions for each of these bins to the recently determined time-varying star formation rate of Gaia DR2 main sequence stars. For white dwarfs in the two lightest mass bins, spanning the mass range 0.95–1.15 M⊙, we find that the cumulative cooling age distribution is statistically consistent with the expectation from the star formation rate. For white dwarfs in the heaviest mass bin, 1.15–1.25 M⊙, we find that their cumulative distribution is inconsistent with the star formation rate for all of the models considered; instead, we find that their cooling age distribution is well fitted by a linear combination of the distribution expected for single stellar evolution products and the distribution expected for double white dwarf merger products when approximately 40–50 per cent of the 1.15–1.25 M⊙ white dwarfs that formed over the past 4 Gyr are produced through double white dwarf mergers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".