An Ultra-Fast Temporal Talbot Array Illuminator
Bibliographic record
Abstract
We propose an all-optical temporal Talbot array illuminator (T-TAI) enabling denoising passive amplification of broadband ($>$10 GHz-bandwidth) optical signals. The key element is an ultra-fast temporal phase grating based on cross-phase modulation. Whereas previous implementations of the T-TAI suffer from the bandwidth limitations inherent to electro-optic systems, the all-optical nature of the proposed approach allows to simultaneously realize high amplification factors and sampling rates, which in turn allows for the efficient processing of broadband signals. In our experiments, we demonstrate a (signal bandwidth) × (amplification factor) product exceeding 370 GHz, more than an order of magnitude improvement as compared with previous electro-optic implementations. We show the prospects of the proposed approach through the recovery of noisy high-speed optical waveforms, namely a$\sim$10 Gbps data signal buried in a much stronger noise background. Additionally, we experimentally demonstrate that the T-TAI samples preserve the full complex information (amplitude and phase) of the incoming signal. The proposed approach should prove useful in multiple fields requiring the detection of weak and noisy broadband signals as well as for other related applications, such as photonics-assisted analog-to-digital conversion.
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 | 0.000 |
| 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 | 0.001 |
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".