Investigating the young AU Mic system with SPIRou: stellar magnetic field and close-in planet mass
Bibliographic record
Abstract
Measuring the mean densities of close-in planets orbiting pre-main-sequence (PMS) stars is crucially needed by planet formation and evolution models. However, PMS stars exhibit intense magnetic activity inducing fluctuations in both photometric and RV curves that overshadow planet signatures. As a result, no close-in planet younger than 25 Myr has a well-constrained bulk density. In this study, we present a spectropolarimetric and velocimetric analysis of 27 near-infrared observations of the nearby active 22 Myr-old red dwarf AU Microscopii collected with SPIRou at the end of the year 2019. We jointly model the planet and stellar activity RV components, resulting in a 3.9σσ-detection of the recently-discovered close-in Neptune-sized planet AU Mic b, with an estimated mass of 17.1+4.7−4.5−4.5+4.7 M⊕⊕, implying a Neptune-like density for the planet. A consistent detection of the planet is independently obtained by simultaneously reconstructing the surface distribution of bright and dark inhomogeneities and estimating the planet parameters using Doppler imaging (DI). Using Zeeman-Doppler Imaging, we invert our time-series of intensity and circularly-polarized line profiles into distributions of brightness and large-scale magnetic field at the surface of the star and explore how these distributions are sheared by latitudinal differential rotation. Finally, we investigate the magnetic activity of AU Mic by computing various indicators and found that the disk-integrated magnetic flux density correlates best with the stellar activity RV signal, in line with recent solar observations.
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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 | 0.001 |
| 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".