Exoplanet Detection Limits of the Athabasca University Robotic Telescope (AURT)
Bibliographic record
Abstract
The discovery and study of exoplanets is an important area of inquiry in the field of astronomy. The transit method is a powerful technique for discovering and studying exoplanets. When the orbital plane of an exoplanet is aligned with the Earth a transit may be observed. As the exoplanet transits, it blocks some of the starlight causing the total flux from the star to drop. With the proper equipment this decrease in the brightness of the star can be measured. The result is a light curve that shows the brightness of the star decreasing as the exoplanet travels in front of it and then returns to its original level when the exoplanet is no longer eclipsing the star. The objectives of this research project are to demonstrate the sensitivity with which exoplanet orbital parameters can be derived using the Athabasca University Robotic Telescope (AURT), and to determine the exoplanet detection limit of this telescope. Satellite survey missions are an important source for finding new exoplanets. But launching satellites is difficult and expensive. Time on space based satellite is also in limited supply. As such there is a need for more Earth based surveys using small telescopes. It is hoped that the work of this research project will be the foundation on which a search for new exoplanets using the AURT can begin. Discipline: Earth and Planetary Sciences Faculty Mentor: Dr. Stefan Cartledge
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 | 0.002 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".