Extended-path intensity correlation: Microarcsecond astrometry with an arcsecond field of view
Bibliographic record
Abstract
We develop in detail a recently proposed optical-path modification of astronomical intensity interferometers. Extended-path intensity correlation (EPIC, see companion paper) introduces a tunable path extension, enabling differential astrometry of multiple compact sources such as stars and quasars at separations of up to a few arcseconds. Combined with other recent technological advances in spectroscopy and fast single-photon detection, a ground-based intensity interferometer array can achieve microarcsecond resolution and even better light-centroiding accuracy on bright sources of magnitude $m\ensuremath{\lesssim}15$. We lay out the theory and technical requirements of EPIC, and discuss the scientific potential. Promising applications include astrometric lensing of stars and quasar images, binary-orbit characterization, exoplanet detection, Galactic acceleration measurements and calibration of the cosmic distance ladder. The introduction of the path extension thus significantly increases the scope of intensity interferometry while reaching unprecedented levels of relative astrometric precision.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".