Bibliographic record
Abstract
Over the past two decades scientists have detected thousands of exoplanets, and their collective properties are now emerging. This thesis contributes to the exoplanet field by analyzing the statistics, formation and stability of exoplanetary systems. The first part of this thesis conducts a statistical reconstruction of the radius and period distributions of Kepler planets. Accounting for observation and detection biases, as well as measurement errors, we calculate the occurrence of planetary systems, including the prevalence of Earth-like planets. This calculation is compared to related works, finding both similarities and differences. Second, the formation of Kepler planets near mean motion resonance (MMR) is investigated. In particular, 27 Kepler systems near 2:1 MMR are analyzed to determine whether tides are a viable mechanism for transporting Kepler planets from MMR. We find that tides alone cannot transport near-resonant planets from exact 2:1 MMR to their observed locations, and other mechanisms must be invoked to explain their formation. Third, a new hybrid integrator HERMES is presented, which is capable of simulating N-bodies undergoing close encounters. HERMES is specifically designed for planets embedded in planetesimal disks, and includes an adaptive routine for optimizing the close encounter boundary to help maintain accuracy. We find the performance of HERMES comparable to other popular hybrid integrators. Fourth, the longterm stability of planetary systems is investigated using machine learning techniques. Typical studies of longterm stability require thousands of realizations to acquire statistically rigorous results, which can take weeks or months to perform. Here we find that a trained machine is capable of quickly and accurately classifying longterm planet stability. Finally, the planetary system HD155358, consisting of two Jovian-sized planets near 2:1 MMR, is investigated using previously collected radial velocity data. New orbital parameters are derived using a Bayesian framework, and we find a high likelihood that the planets are in MMR. In addition, formation and stability constraints are placed on the HD155358 system.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".