Magnetic Fields in M-dwarf Members of the Pleiades Open Cluster Using APOGEE Spectra
Bibliographic record
Abstract
Abstract Average magnetic field measurements are presented for 62 M-dwarf members of the Pleiades open cluster, derived from Zeeman-enhanced Fe i lines in the H band. A Markov Chain Monte Carlo methodology was employed to model magnetic filling factors using Sloan Digital Sky Survey (SDSS) IV APOGEE high-resolution spectra, along with the radiative transfer code Synmast, MARCS stellar atmosphere models, and the APOGEE Data Release 17 spectral line list. There is a positive correlation between mean magnetic fields and stellar rotation, with slow-rotator stars (Rossby number, Ro > 0.13) exhibiting a steeper slope than rapid rotators (Ro < 0.13). However, the latter sample still shows a positive trend between Ro and magnetic fields, which is given by 〈 B 〉 = 1604 × Ro −0.20 . The derived stellar radii when compared with physical isochrones show that, on average, our sample shows radius inflation, with median enhanced radii ranging from +3.0% to +7.0%, depending on the model. There is a positive correlation between magnetic field strength and radius inflation, as well as with stellar spot coverage, correlations which together indicate that stellar spot-filling factors generated by strong magnetic fields might be the mechanism that drives radius inflation in these stars. We also compare our derived magnetic fields with chromospheric emission lines (H α , H β , and Ca ii K), as well as with X-ray and H α to bolometric luminosity ratios, and find that stars with higher chromospheric and coronal activity tend to be more magnetic.
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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 | 0.000 |
| 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".