High‐Resolution Time‐Correlated Single‐Photon Counting Using Electro‐Optic Sampling
Bibliographic record
Abstract
Abstract A simple, practical method based on electro‐optic gating is experimentally shown to improve the temporal resolution of single‐photon detection by more than 16 times. Delay times between ultrafast single photons and a reference clock are stretched by a desired programmable sampling gate factor, allowing reconstruction of delay histograms with ≈0.001 photons per pulse to within 60 ps. By transferring the bandwidth of RF electronics to single‐photon counting, complex single‐photon signals with large time‐bandwidth products > 2000 are temporally stretched up so that they can be resolved by slow detectors, irrespective of the quality of the detector instrument response function. This method is also applied to biphotons, in order to reconstruct the 2D histogram of joint detection delays with 15 times better resolution. The phenomenon of nonlocal dispersion, which is not resolvable directly with the slow detectors, is then observable to within the 98 ps level. The proof‐of‐concept demonstration uses off‐the‐shelf commercial fiber‐integrated LiNbO3 modulators and RF electronics, and the method is readily integrable on‐chip and to speeds >100 GHz, offering a practical solution to ultrafast time‐correlated single‐photon counting beyond the research laboratory.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".