Giant planets around young active stars
Bibliographic record
Abstract
Although our understanding of how planetary systems come to exist has drastically improved over the last two decades, there is still plenty of research to be made around the formation and evolution of massive, short-orbit exoplanets e.g. hot Jupiters, warm Neptunes. Do they form through migration mechanism or through in-situ formation? By better understanding the occurrence rates of these types of planets in every stage of a star’s life, we can better understand their fate and hope to favour one formation/evolution mechanism over another. In this context, it is crucial to estimate the population of these weird planets around young stars. Unfortunately, probing young stars is notoriously difficult due to their strong magnetic activity (or ‘jitter’) inducing spurious radial velocity (RV) signals that often mask the planet(s) RV signature(s) used to detect them. In Heitzmann et al. 2021 (in prep), we attempted to recover simulated planetary signatures hidden behind real stellar RV data exhibiting large jitter. We used two distinct activity-filtering techniques (Doppler imaging and Gaussian process) and found that it is possible to hunt for short-orbit gas giants in existing data, gathered using non-stabilised spectrograph in the scope of stellar surveys.
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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 | 0.000 | 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) | 0.001 | 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".