The colibri telescope array for KBO detection through serendipitous stellar occultations: A technical description
Bibliographic record
Abstract
We present the technical design, construction and testing of the Colibri telescope array at Elginfield Observatory near London, Ontario, Canada. Three 50-cm telescopes are arranged in a triangular array and are separated by 110–160 m. During operation, they will monitor field stars at the intersections of the ecliptic and galactic plane for serendipitous stellar occultations (SSOs) by trans-Neptunian objects (TNOs). At a frame rate of 40 frames per second (fps), Fresnel diffraction in the occultation light curve can be resolved and, with coincident detections, be used to estimate basic properties of the occulting object. Using off-the-shelf components, the Colibri system streams imagery to disk at a rate of 1.5 GB/s for next-day processing by a custom occultation detection pipeline. The imaging system has been tested and is found to perform well, given the moderate site conditions. Limiting magnitudes at 40 fps are found to be about 12.1 (temporal SNR = 5, visible light Gaia G band) with time-series standard deviations ranging from about 0.035 mag to >0.2 mag. SNR is observed to decrease linearly with magnitude for stars fainter than about G = 9.5 mag. Brighter than this limit, SNR is constant, suggesting that atmospheric scintillation is the dominant noise source. Astrometric solutions show errors typically less than ±0.3 pixels (0.8 arc seconds) without a need for high-order corrections.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.013 |
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".