The TAOS II Survey: Real-time Detection and Characterization of Occultation Events
Bibliographic record
Abstract
Abstract The Transneptunian Automated Occultation Survey (TAOS II) is a blind occultation survey with the aim of measuring the size distribution of Trans-Neptunian Objects with diameters in the range of 0.3 ≲ D ≲ 30 km. TAOS II will observe as many as 10,000 stars at a cadence of 20 Hz with all three telescopes simultaneously. This will produce up to ∼20 billion photometric measurements per night, and as many as ∼6 trillion measurements per year, corresponding to over 70 million individual light curves. A very fast analysis pipeline for event detection and characterization is needed to handle this massive data set. The pipeline should be capable of real-time detection of events (within 24 hours of observations) for follow-up observations of any occultations by larger TNOs. In addition, the pipeline should be fast and scalable for large simulations where simulated events are added to the observed light curves to measure detection efficiency and biases in event characterization. Finally, the pipeline should provide estimates of the size of and distance to any occulting objects, including those with non-spherical shapes. This paper describes a new data analysis pipeline for the detection and characterization of occultation events.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".