Photonic Generation of Chirped Millimeter-Wave Pulses Based on Nonlinear Frequency-to-Time Mapping in a Nonlinearly Chirped Fiber Bragg Grating
Bibliographic record
Abstract
A novel approach to optically generating chirped millimeter-wave pulses with tunable chirp rate based on spectral shaping and nonlinear frequency-to-time mapping is proposed and experimentally demonstrated. In the proposed approach, the optical power spectrum of an ultrashort pulse from a femtosecond pulsed laser is shaped by a two-tap Sagnac loop filter that has a sinusoidal frequency response. The spectrum-shaped optical pulse is then sent to a nonlinearly chirped fiber Bragg grating (NL-CFBG) with a tunable nonlinear group delay to serve as a high-order dispersive device to perform the nonlinear frequency-to-time mapping. A chirped electrical pulse with a high central frequency and large chirp rate is then generated at the output of a high-speed photodetector. The NL-CFBG used in the proposed system is produced from a regular linearly chirped fiber Bragg grating based on strain-gradient beam tuning. A detailed theoretical analysis on the chirped pulse generation is developed, which is verified by numerical simulations and experiments. Millimeter-wave pulses with a central frequency of around 35 GHz and instantaneous frequency chirp rates of 0.053 and 0.074 GHz/ps are experimentally generated.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".