Comparison of methods used to derive the Galactic star formation history from white dwarf samples
Bibliographic record
Abstract
ABSTRACT We compare three methods of deriving the local Galactic star formation history, using as a benchmark the Gaia-defined 40 pc white dwarf sample, currently the largest volume complete sample of stellar remnants with medium-resolution spectroscopy. We create a population synthesis model to (1) reproduce the observed white dwarf luminosity function, (2) reproduce the observed absolute Gaia G magnitude distribution, and (3) directly calculate the ages of all individual white dwarfs in the 40 pc volume. We then compare the star formation histories determined from each method. Previous studies using these methods were based on different white dwarf samples, and as such, were difficult to compare. Uncertainties in each method, such as the initial mass function, initial–final mass relation, main-sequence lifetimes, stellar metallicity, white dwarf cooling ages and binary evolution, are accounted for to estimate the precision and accuracy of each method. We conclude that no method is quantitatively better at determining the star formation history, and all three produce star formation histories that agree within uncertainties of current external astrophysical relations.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".