Searching for the weakest detectable magnetic fields in white dwarfs
Bibliographic record
Abstract
Our knowledge of the magnetism in white dwarfs is based on an observational dataset that is biased in favour of stars with very strong magnetic fields. Most of the field measurements available in the literature have a relatively low sensitivity, while current instruments allow us to detect magnetic fields of white dwarfs with sub-kG precision. With the aim of obtaining a more complete view of the incidence of magnetic fields in degenerate stars, we have started a long-term campaign of high-precision spectropolarimetric observations of white dwarfs. Here we report the results obtained so far with the low-resolution FORS2 instrument of the ESO VLT and the medium-resolution ISIS instrument of the WHT. We have considered a sample of 48 stars, of which five are known magnetic or suspected magnetic stars, and obtained new longitudinal magnetic field measurements with a mean uncertainty of about 0.6 kG. Overall, in the course of our survey (the results of which have been partially published in papers devoted to individual stars) we have discovered one new weak-field magnetic white dwarf, confirmed the magnetic nature of another, found that a suspected magnetic star is not magnetic, and suggested two new candidate magnetic white dwarfs. Even combined with data previously obtained in the literature, our sample is not sufficient yet to reach any final conclusions about the actual incidence of very weak magnetic fields in white dwarfs, but we have set the basis to achieve a homogeneous survey of an unbiased sample of white dwarfs. As a by-product, our survey has also enabled us to carry out a detailed characterisation of the ISIS and the FORS2 instruments for the detection of extremely weak magnetic fields in white dwarfs, and in particular to relate the signal-to-noise ratio to measurement uncertainty for white dwarfs of different spectral types. This study will help the optimisation of future observations.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".