Caractériser les propriétés atmosphériques de naines rouges à partir de spectres obtenus avec SPIRou
Bibliographic record
Abstract
Over the last decades, M dwarfs have attracted increasing attention and were identified as targets of choice for the hunt of exoplanets located in the habitable zone of their host star, and for the study of magnetic fields in cool stars. Nonetheless, their study still represents a great technical and scientific challenge because of the intrinsic faintness of these stars. This thesis is dedicated to the study of M dwarfs, and in particular to their characterization from high-resolution and high signal-to-noise ratio near-infrared spectra acquired with the spectro-polarimeter SPIRou installed at the Canada-France- Hawaï Telescope. We use state-of-the-art synthetic spectra to constrain the atmospheric parameters of M dwarfs, taking advantage of the large wavelength coverage of SPIRou. We show how the different models and line lists considered can lead to significant discrepancies in the estimation of atmospheric parameters. With our process we are able to constrain such parameters with a precision of about 30 K in effective temperature (Teff), and 0.1 dex in surface gravity (log g) and metallicity ([M/H]). We also illustrate that the synthetic spectra computed from MARCS model atmospheres can be used to constrain the abundance of alpha elements, and we derive estimates of atmospheric parameters for 44 M dwarfs observed in the context of the SPIRou Legacy Survey (SLS). Finally, we turn our focus to magnetic targets, and introduce ZeeTurbo, our new code based on the extensively-used Turbospectrum, to which we added polarized radiative transfer capabilities to include the effect of magnetic fields on spectra in our modeling. With this new code, and an adapted analysis, we constrain the average surface magnetic flux in addition to the atmospheric parameters of several magnetic targets observed in the context of the SLS. Our results and developed tools will guide future projects aimed at characterizing stars, estimating elemental abundances, and constraining magnetic fields, taking advantage of the long lasting observations carried out in the framework of the SLS, and its follow-up SPICE.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".