The White Dwarf Opportunity
Bibliographic record
Abstract
The winds of M dwarf stars are poorly constrained and poorly understood. Literature values of M dwarf wind mass-loss rates span several orders of magnitude and suffer from poor number statistics, with fewer than 30 systems having well-constrained values. Stellar winds are especially important to how young planetary systems evolve, with consequences for expected exoplanet atmosphere loss, atmospheric chemistry, surface habitability, and more. While several methodologies exist for constraining stellar winds, only four have produced detections of wind mass-loss rates for M dwarf stars. One of these methodologies involves constraining the M dwarf mass-loss rate using atmospheric metal pollution of a close companion white dwarf star. In this work, I calculate wind mass-loss rates for two M dwarf stars using this methodology. Additionally, I expand the range of systems to which this methodology can be applied. Previous studies have noted that M dwarfs in close binary systems with white dwarfs are often magnetically active. This magnetic activity produces emission lines for magnetically sensitive elements, such as calcium. Calcium is also the metal pollutant that produces the deepest optical light absorption signals in white dwarfs. In this work, I develop methodology for recovering calcium absorption equivalent widths from white dwarf stars in unresolved binaries with magnetically active M dwarfs. I apply this methodology to 56 systems, recovering 19 white dwarf calcium equivalent widths which remain in absorption within their 1 sigma uncertainty. While it is left to a future work to calculate wind rates for these systems, this methodology does significantly expand the number of close white dwarf-M dwarf binary systems for which M dwarf wind rates can be recovered.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.013 |
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".