Proving the outstanding capabilities of Imaging Atmospheric Cherenkov Telescopes in high time resolution optical astronomy
Bibliographic record
Abstract
Imaging Atmospheric Cherenkov Telescopes (IACTs) are very-large telescopes designed to detect the nanosecond-timescale flashes produced within extended air showers. Because IACTs are sensitive to the Cherenkov light (UV/blue) and use photodetectors with extremely fast time responses, they are also able to perform simultaneous optical observations. The large reflecting areas of these telescopes (larger than 100 m$^2$) makes them well-suited to studying fast optical transient phenomena with timescales ranging from seconds to milliseconds to nanoseconds, and the unique optical design provides a wide field of view monitoring capability with a modest point spread function. VERITAS, with its recently upgraded PMT current monitoring instrumentation, was able to provide the first detection of asteroid occultations with an IACT, resulting in the highest angular resolution measurements for stellar diameters ever taken in the visible band range. Here we explore the feasibility of using this technique to significantly expand the number of stars with directly measured stellar radii, usable for population studies to test stellar evolution modelling or transiting exoplanet radius measurements. A single observatory with a high-speed visible-band photometer with a sensitivity reaching the 13$^{th}$ magnitude could increase the number of directly measured K stars diameters by 50%.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".