Demonstration of stellar intensity interferometry with the four VERITAS telescopes
Bibliographic record
Abstract
High angular resolution observations at optical wavelengths provide valuable insights into stellar astrophysics1,2, and enable direct measurements of fundamental stellar parameters3,4 and the probing of stellar atmospheres, circumstellar disks5, the elongation of rapidly rotating stars6 and the pulsations of Cepheid variable stars7. The angular size of most stars is of the order of one milliarcsecond or less, and to spatially resolve stellar disks and features at this scale requires an optical interferometer using an array of telescopes with baselines on the order of hundreds of metres. We report on the implementation of a stellar intensity interferometry system developed for the four VERITAS imaging atmospheric Cherenkov telescopes. The system was used to measure the angular diameter of the two sub-milliarcsecond stars β Canis Majoris and ϵ Orionis with a precision of greater than 5%. The system uses an offline approach in which starlight intensity fluctuations that are recorded at each telescope are correlated post observation. The technique can be readily scaled onto tens to hundreds of telescopes, providing a capability that has proven technically challenging to the current generation of optical amplitude interferometry observatories. This work demonstrates the feasibility of performing astrophysical measurements using imaging atmospheric Cherenkov telescope arrays as intensity interferometers and shows the promise for integrating an intensity interferometry system within future observatories such as the Cherenkov Telescope Array.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".