Planets around young active solar-type stars: assessing detection capabilities from a non-stabilized spectrograph
Bibliographic record
Abstract
ABSTRACT Short-orbit gas giant planet formation/evolution mechanisms are still not well understood. One promising pathway to discriminate between mechanisms is to constrain the occurrence rate of these peculiar exoplanets at the earliest stage of the system’s life. However, a major limitation when studying newly born stars is stellar activity. This cocktail of phenomena triggered by fast rotation, strong magnetic fields, and complex internal dynamics, especially present in very young stars, compromises our ability to detect exoplanets. In this paper, we investigated the limitations of such detections in the context of already acquired data solely using radial velocity data acquired with a non-stabilized spectrograph. We employed two strategies: Doppler Imaging and Gaussian Processes and could confidently detect hot Jupiters with a semi-amplitude of 100 m s−1 buried in the stellar activity. We also showed the advantages of the Gaussian Process approach in this case. This study serves as a proof of concept to identify potential candidates for follow-up observations or even discover such planets in legacy data sets available to the community.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".