wdwarfdate: A Python Package to Derive Bayesian Ages of White Dwarfs
Bibliographic record
Abstract
Abstract White dwarfs have been successfully used as cosmochronometers in the literature; however, their reach has been limited in comparison to their potential. We present wdwarfdate, a publicly available Python package to derive the Bayesian age of a white dwarf, based on its effective temperature (T eff) and surface gravity (log g). We make this software easy to use with the goal of transforming the usage of white dwarfs as cosmochronometers into an accessible tool. The code estimates the mass and cooling age of the white dwarf, as well as the mass and main-sequence age of the progenitor star, allowing for a determination of the total age of the object. We test the reliability of the method by estimating the parameters of white dwarfs from previous studies and find agreement with the literature within measurement errors. In addition, we use wdwarfdate to infer the total age of 18 white dwarfs that are in possible wide binaries with M dwarfs; this leads to an additional set of age-calibrated low-mass stars. By analyzing the limitation of the code we find a typical uncertainty of 10% on the total age when both input parameters have uncertainties of 1%, and an uncertainty of 25% on the total age when T eff has an uncertainty of 10% and log g of 1%. Furthermore, wdwarfdate assumes single-star evolution and can be applied to calculate the total age of a white dwarf with parameters in the range 1500 ≲ T eff ≲ 90,000 K and 7.9 ≲ log g ≲ 9.3. Finally, the code assumes a uniform mixture of C/O in the core and single-star evolution, which is reliable in the range of white dwarf masses 0.45−1.1 M ⊙(7.73 ≲ log g ≲ 8.8).
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.016 |
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".