Noise resilient and accurate target detection using fractional-order Fourier domain correlation
Bibliographic record
Abstract
Quantum enhanced optical target detection provides a unique route to increased noise resilience of classical LiDARs (laser imaging, detection, and ranging) by using time correlation of non-classical photon pairs. Such enhancement is dictated by the detector temporal uncertainty that is typically orders of magnitude larger than the intrinsic correlation time. To circumvent such detector limitation, we explore the possibility of measuring correlation in the fractional-order Fourier domain (FrFD), which can be realized with the non-local dispersion cancellation. Experimentally, we verify this principle using a fiber-coupled waveguide source of photon pairs, showing enhanced noise rejection as compared with conventional time-domain coincidence detection and classical intensity detection. For false alarm rates of 10 −9 , an 89 dB improved detection rate is measured using receiver operating characteristics when comparing our FrFD protocol with classical intensity detection. Additionally, we discussed the resilience of FrFD correlation against intentionally prepared counterfeit signal photons. The possibility enabled by measuring correlation in the FrFD should also provide potential benefit for various sensing and communication protocols that relies on coincidence detection.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".