The eccentricity distribution of warm sub-Saturns in <i>TESS</i>
Bibliographic record
Abstract
ABSTRACT We present the eccentricity distribution of warm sub-Saturns (4–8 $R_{\rm{\oplus }}$, 8–200 d periods) as derived from an analysis of transit light curves from NASA’s Transiting Exoplanet Survey Satellite (TESS) mission. We use the ‘photoeccentric’ effect to constrain the eccentricities of 76 planets, comprising 60 and 16 from single and multitransiting systems, respectively. We employ Hierarchical Bayesian Modelling to infer the eccentricity distribution of the population, testing both a Beta and Mixture Beta distribution. We identify a few highly eccentric (${e\sim 0.7-0.8}$) warm sub-Saturns with eccentricities that appear too high to be explained by disc migration or planet–planet scattering alone, suggesting high-eccentricity migration may play a role in their formation. The majority of the population have a mean eccentricity of $\bar{e} = 0.103^{+0.047}_{-0.045}$, consistent with both planet–disc and planet–planet interactions. Notably, we find that the highly eccentric sub-Saturns occur in single-transiting systems. This study presents the first evidence at the population level that the eccentricities of sub-Saturns may be sculpted by dynamical processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".