The Canada–France Imaging Survey: Reconstructing the Milky Way Star Formation History from Its White Dwarf Population
Bibliographic record
Abstract
Abstract As the remnants of stars with initial masses ≲8 M ⊙, white dwarfs contain valuable information on the formation histories of stellar populations. In this paper, we use deep, high-quality, u-band photometry from the Canada–France Imaging Survey, griz photometry from Pan-STARRS1, as well as proper motions from Gaia DR2, to select 25,156 white dwarf candidates over ∼4500 deg2 using a reduced proper motion diagram. We develop a new white dwarf population synthesis code that returns mock observations of the Galactic field white dwarf population for a given star formation history, while simultaneously taking into account the geometry of the Milky Way (MW), survey parameters, and selection effects. We use this model to derive the star formation histories of the thin disk, thick disk, and stellar halo. Our results show that the MW disk began forming stars (11.3 ± 0.5) Gyr ago, with a peak rate of (8.8 ± 1.4) M ⊙ yr −1 at (9.8 ± 0.4) Gyr, before a slow decline to a constant rate until the present day—consistent with recent results suggesting a merging event with a satellite galaxy. Studying the residuals between the data and best-fit model shows evidence for a slight increase in star formation over the past 3 Gyr. We fit the local fraction of helium-atmosphere white dwarfs to be (21 ± 3)%. Incorporating this methodology with data from future wide-field surveys such as the Large Synoptic Survey Telescope, Euclid, The Cosmological Advanced Survey Telescope for Optical and ultraviolet Research, and the Wide Field Infrared Survey Telescope should provide an unprecedented view into the formation of the MW at its earliest epoch through its white dwarfs.
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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 | 0.000 | 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".