Estimating fundamental parameters of nearby M dwarfs from SPIRou spectra
Bibliographic record
Abstract
ABSTRACT We present the results of a study aiming at retrieving the fundamental parameters of M dwarfs from spectra secured with SPIRou, the near-infrared high-resolution spectropolarimeter installed at the Canada–France–Hawaii Telescope (CFHT), in the framework of the SPIRou Legacy Survey (SLS). Our study relies on comparing observed spectra with two grids of synthetic spectra, respectively, computed from PHOENIX and MARCS model atmospheres, with the ultimate goal of optimizing the precision at which fundamental parameters can be determined. In this first step, we applied our technique to 12 inactive M dwarfs with effective temperatures (Teff) ranging from 3000 to 4000 K. We implemented a benchmark to carry out a comparison of the two models used in this study. We report that the choice of model has a significant impact on the results and may lead to discrepancies in the derived parameters of 30 K in Teff and 0.05 dex to 0.10 dex in surface gravity (log g) and metallicity ($\rm {[M/H]}$), as well as systematic shifts of up to 50 K in Teff and 0.4 dex log g and $\rm {[M/H]}$. The analysis is performed on high signal-to-noise ratio template SPIRou spectra, averaged over multiple observations corrected from telluric absorption features and sky lines, using both a synthetic telluric transmission model and principal component analysis. With both models, we retrieve Teff, log g, and $\rm {[M/H]}$ estimates in good agreement with reference literature studies, with internal error bars of about 30 K, 0.05 dex, and 0.1 dex, respectively.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".