Estimating the atmospheric properties of 44 M dwarfs from SPIRou spectra
Bibliographic record
Abstract
ABSTRACT We describe advances on a method designed to derive accurate parameters of M dwarfs. Our analysis consists in comparing high-resolution infrared spectra acquired with the near-infrared spectro-polarimeter SPIRou to synthetic spectra computed from MARCS model atmospheres, in order to derive the effective temperature (Teff), surface gravity (log g), metallicity ($\rm {[M/H]}$), and alpha-enhancement ($\rm {[\alpha /Fe]}$) of 44 M dwarfs monitored within the SPIRou Legacy Survey (SLS). Relying on 12 of these stars, we calibrated our method by refining our selection of well-modelled stellar lines, and adjusted the line list parameters to improve the fit when necessary. Our retrieved Teff, log g, and $\rm {[M/H]}$ are in good agreement with literature values, with dispersions of the order of 50 K in Teff and 0.1 dex in log g and $\rm {[M/H]}$. We report that fitting $\rm {[\alpha /Fe]}$ has an impact on the derivation of the other stellar parameters, motivating us to extend our fitting procedure to this additional parameter. We find that our retrieved $\rm {[\alpha /Fe]}$ are compatible with those expected from empirical relations derived in other studies.
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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 | 0.001 |
| 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".