Searching for Planets and Moons Using Gravitational Dynamics
Bibliographic record
Abstract
The search for planets around stars other than our Sun, known as "exoplanets", has made use of a variety of different methods. Some methods look for the planet itself through direct imaging. Other techniques search for the signature of planets in the light from its parent star. Such indicators of a planet include the dimming of the star as the planet passes in front of it (the Transit method) or the discrepancy in events that occur from the influence of unseen planets (the Transit Timing Variation method). These techniques allow the discovery of worlds not otherwise visible to telescopes, through the orbital dynamics of known worlds. This work pursues the premise of detecting hidden celestial bodies through their effect upon visible ones. In the first chapter, I examine the usefulness of the Canadian space telescope, the Near Earth Object Surveillance Satellite (NEOSSat), as a tool for exoplanetary science. I performe follow-up observations of several targets from the Transiting Exoplanet Survey Satellite (TESS). These observations improve the orbital ephemerides and baselines for these ex- oplanets, as well as demonstrate the capabilities of NEOSSat as a tool for exoplanetary science. In the second chapter, I examine whether moons around exoplanets ("exomoons") could be detected via the transit timing variations they exert upon their planet. Exomoons are exceptionally difficult to detect via transits, but an exomoon could reveal itself through the gravitational effect it has upon on its parent planet. Thirteen Kepler systems are explored to determine whether this hypothesis could hold. The observed behaviour of eight systems is consistent with the presence of an exomoon, though this is insufficient to confirm the existence of a moon. In the third chapter, I examine the debris disk around HD 181327. It shows a significant asymmetry in its surface brightness profile when viewed in visible light. By performing N-body simulations, I find that a 2-5 Jupiter-mass planet on a circular orbit at 62 au could produce and maintain a similar feature to that observed. Gravity is the universal architect of planetary systems. The existence of a hidden celestial body can be betrayed by its gravitational influence upon another. In this thesis, I explore new ways of using gravity in this vein.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".