Augmentation of VERITAS Telescopes for Stellar Intensity Interferometry
Bibliographic record
Abstract
In 2018-2019 the VERITAS VHE gamma-ray observatory was augmented with high-speed optical instrumentation and continuous data recording electronics to create a sensitive Stellar Intensity Interferometry (SII) observatory, VERITAS-SII. The primary science goal of VERITAS-SII is to perform stellar diameter measurements and image analysis in the visible wavebands on a selection of bright (m< 6), hot (O/B/A) stars. The VERITAS Collaboration has agreed to the deployment and operation of VERITAS-SII during several days each month around the full moon period when VERITAS does not perform VHE gamma-ray observations. The VERITAS-SII augmentation employs custom high-speed/low-noise focal plane instrumentation using high quantum efficiency photomultiplier tubes, and a battery-powered, fiber-optic controlled High Voltage supply. To reduce engineering time, VERITAS-SII uses commercially available high-speed (250 MS/sec), continuously streaming electronics to record the time dependence of the intensity fluctuations at each VERITAS telescope. VERITAS-SII also uses fast ( < 100 psec) data acquisition clock synchronization over inter-telescope distances (greater than 100 m) using a commercially available White Rabbit based timing solution. VERITAS-SII is now in full operation at the VERITAS observatory, F.L. Whipple Observatory, Amado, AZ USA. This paper describes the design of the instrumentation hardware used for VERITAS-SII augmentation of the VERITAS observatory, the status of initial VERITAS-SII observations, and plans for future improvements to VERITAS-SII.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".