Bibliographic record
Abstract
Context. The rotation period of stars is an important parameter along with mass, radius, effective temperature. It is an essential parameter for any radial velocity monitoring, as stellar activity can mimic the presence of a planet at the stellar rotation period. Several methods exist to measure it, including long sequences of photometric measurements or temporal series of stellar activity indicators. Aims. Here, we use the circular polarization in near-infrared spectral lines for a sample of 43 quiet M dwarfs and compare the measured rotation periods to those obtained with other methods. Methods. From Stokes V spectropolarimetric sequences observed with SPIRou at CFHT and the data processed with the APERO pipeline, we compute the least squares deconvolution profiles using different masks of atomic stellar lines with known Land\'e factor appropriate to the effective temperature of the star. We derive the longitudinal magnetic field to examine its possible variation along the 50 to 200 observations of each star. For determining the stellar rotation period, we apply a Gaussian process regression enabling us to determine the rotation period of stars with evolving longitudinal field. Results. Among the 43 stars of our sample, we were able to measure a rotation period for 27 stars. For 8 stars, the rotation period was previously unknown. We find a good agreement of our rotation periods with periods found in the literature based on photometry and activity indicators and confirm that near-infrared spectropolarimetry is an important tool to measure rotation periods, even for magnetically quiet stars. Furthermore, we compute ages for 20 stars of our sample using gyrochronology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".