SEARCHING FOR TROJAN ASTEROIDS IN THE HD 209458 SYSTEM: SPACE-BASED<i>MOST</i>PHOTOMETRY AND DYNAMICAL MODELING
Bibliographic record
Abstract
We have searched Microvariability and Oscillations of Stars ( MOST ) satellite photometry obtained in 2004, 2005, and 2007 of the solar-type star HD 209458 for Trojan asteroid swarms dynamically coupled with the system's transiting "hot Jupiter" HD 209458b. Observations of the presence and nature of asteroids around other stars would provide unique constraints on migration models of exoplanetary systems. Our results set an upper limit on the optical depth of Trojans in the HD 209458 system that can be used to guide current and future searches of similar systems by upcoming missions. Using cross-correlation methods with artificial signals implanted in the data, we find that our detection limit corresponds to a relative Trojan transit depth of 1 × 10 −4 , equivalent to ∼1 lunar mass of asteroids, assuming power-law Trojan size distributions similar to Jupiter's Trojans in our solar system. We confirm with dynamical interpretations that some asteroids could have migrated inward with the planet to its current orbit at 0.045 AU, and that the Yarkovsky effect is ineffective at eliminating objects of >1 m in size. However, using numerical models of collisional evolution we find that, due to high relative speeds in this confined Trojan environment, collisions destroy the vast majority of the asteroids in <10 Myr. Our modeling indicates that the best candidates to search for exoTrojan swarms in 1:1 mean resonance orbits with "hot Jupiters" are young systems (ages of about 1 Myr or less). Years of Kepler satellite monitoring of such a system could detect an asteroid swarm with a predicted transit depth of 3 × 10 −7 .
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 | 0.000 | 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) | 0.000 | 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".