White Dwarfs in the Era of the LSST and Its Synergies with Space-based Missions
Bibliographic record
Abstract
Abstract With the imminent start of the Legacy Survey for Space and Time (LSST) at the Vera C. Rubin Observatory and several new space telescopes expected to begin operations later in this decade, both time-domain and wide-field astronomy are on the threshold of a new era. In this paper, we use a new multicomponent model for the distribution of white dwarfs (WDs) in our Galaxy to simulate the WD populations in four upcoming wide-field surveys (i.e., LSST, Euclid, the Roman Space Telescope, and the Cosmological Advanced Survey Telescope for Optical and uv Research) and use the resulting samples to explore some representative WD science cases. Our results confirm that LSST will provide a wealth of information for Galactic WDs, detecting more than 150 million WDs at the final depth of its stacked 10 yr survey. Within this sample, nearly 300,000 objects will have 5σ parallax measurements, and nearly 7 million will have 5σ proper-motion measurements, allowing the detection of the turnoff in the halo WD luminosity function and the discovery of more than 200,000 ZZ Ceti stars. The wide wavelength coverage that will be possible by combining LSST data with observations from Euclid and/or the Roman Space Telescope will also discover WDs with debris disks, highlighting the advantages of combining data between the ground- and space-based missions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".