Exo-Dragonfly: Adapting the Dragonfly Telephoto Array to the Observation of Exoplanet Transits
Bibliographic record
Abstract
Abstract We present the process and results of the Exo-Dragonfly project, an undertaking to adapt and use the Dragonfly Telephoto Array to observe exoplanet transit light curves. At the time of the project, the Dragonfly instrument, located in New Mexico, USA, was composed of 48 commercial 143 mm aperture telephoto lenses, split across two mounts and simultaneously observing the same field with r and g filters. The setup had a photon collection area equivalent to a 1 m diameter lens. With the driving goal of producing observations in support of the Transiting Exoplanet Survey Satellite follow-up efforts, we developed an automatic observation scheduling process, a new observing mode for time-sensitive time series observation, and a reduction/analysis pipeline to process data. Our results show that the Dragonfly Telephoto Array can achieve a photometric precision floor of ∼0.5 ppt for targets in the magnitude range of 8.5 ≲ m V ≲ 13 for 4–5 minutes bins. We discuss the successes and challenges encountered while using this unique multi-camera telescope as well as suggestions for improvements of this (or other similar) instruments as they pertain to exoplanet transit observations moving forward.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".