The True Incidence of Magnetism Among Field White Dwarfs
Bibliographic record
Abstract
We study the incidence of magnetism in white dwarfs from three large and well-observed samples of hot, cool, and nearby white dwarfs in order to test whether the fraction of magnetic degenerates is biased and whether it varies with effective temperature, cooling age, or distance. The magnetic fraction is considerably higher for the cool sample of Bergeron, Ruiz, & Leggett and the Holberg, Oswalt, & Sion sample of local white dwarfs than it is for the generally hotter white dwarfs of the Palomar-Green survey. We show that the mean mass of magnetic white dwarfs in this survey is 0.93 M ⊙ or more, so there may be a strong bias against their selection in the magnitude-limited Palomar-Green survey. We argue that this bias is not as important in the samples of cool and nearby white dwarfs. However, this bias may not account for all of the difference in the magnetic fractions of these samples. It is not clear that the magnetic white dwarfs in the cool and local samples are drawn from the same population as the hotter PG stars. In particular, two or three of the cool sample are low-mass white dwarfs in unresolved binary systems. Moreover, there is a suggestion from the local sample that the fractional incidence may increase with decreasing temperature, luminosity, and/or cooling age. Overall, the true incidence of magnetism at the level of ∼2 MG or greater is at least ∼10%, and it could be higher. Limited studies capable of detecting lower field strengths down to ∼10 kG suggest by implication that the total fraction may be substantially higher then 10%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".