Classical imaging with undetected photons using four-wave mixing in silicon core fibers
Bibliographic record
Abstract
Undetected-photon imaging allows for objects to be imaged in wavelength regions where traditional components are unavailable. Although first demonstrated using quantum sources, recent work has shown that the technique also holds with classical beams. To date, however, all the research in this area has exploited parametric down-conversion processes using bulk nonlinear crystals within free-space systems. Here, we demonstrate undetected-photon-based imaging using light generated via stimulated four-wave mixing within highly nonlinear silicon fiber waveguides. The silicon fibers have been tapered to have a core diameter of ∼ 915 nm to engineer the dispersion and reduce the insertion losses, allowing for tight mode confinement over extended lengths to achieve practical nonlinear conversion efficiencies ( ∼ − 30 dB ) with modest pump powers ( ∼ 48 mW ). Both amplitude and phase images are obtained using classically generated light, confirming the high degree of spatial and phase correlation of our system. The high powers ( > 10 nW ) and long coherence lengths ( > 4 km ) associated with our large fiber-based system result in high contrast and stable images.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".