Very long baseline interferometry and observations of gravitational lenses using intensity fluctuations: an analysis based on intensity autocorrelation
Bibliographic record
Abstract
A novel interferometric technique that uses the spectrum of the current fluctuations of a quadratic detector, a type of detector commonly used in Astronomy, has recently been introduced. It has major advantages with respect to classical interferometry. It can be used to observe gravitational lenses that cannot be detected with standard techniques. It can be used to carry out very long baseline interferometry. Although the original theoretical analysis, that uses wave interaction effects, is rigorous, it is not easy to understand. The present article therefore carries out a simpler analysis, using the autocorrelation of intensity fluctuations, which is easier to understand. It is based on published experiments that were carried out to validate the original theory. The autocorrelation analysis also validates simple numerical techniques, based on the autocorrelation, to model the angular intensity distribution of a source. The autocorrelation technique also allows a much simpler detection of the signal. In practice, the gravitational lens applications are the ones that can readily be done with presently available telescopes. We describe a practical example that shows that presently available VLBI radio-astronomical data can be used to observe microlensisng and millilensing in macrolensed Quasars. They may give information on the dark matter substructures in the lensing galaxies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".