Transform-limited picosecond pulse shaping based on temporal coherence synthesization
Bibliographic record
Abstract
A simple and efficient optical pulse re-shaper based on the concept of temporal coherence synthesization is proposed and analyzed in detail. Specifically, we demonstrate that an arbitrary chirp-free (transform-limited) optical pulse waveform can be synthesized from a given transform-limited Gaussian-like input optical pulse by coherently superposing a set of properly delayed replicas of this input pulse, e.g. using a conventional multi-arm interferometer. A practical implementation of this general concept based on the use of conventional concatenated two-arm interferometers is also suggested and demonstrated. This specific implementation allows the synthesis of any desired temporally-symmetric optical waveform with time features only limited by the input pulse bandwidth. A general optimization algorithm has been developed and applied for designing the system specifications (number of interferometers and relative time delays in these interferometers) that are required to achieve a desired optical pulse re-shaping operation. The required tolerances in this system have been also estimated and confirmed by numerical simulations. The proposed technique has been experimentally demonstrated by re-shaping an approximately 1-ps Gaussian-like optical pulse into various temporal shapes of practical interest, i.e. picosecond transform-limited flat-top, parabolic and triangular pulses (all centered at a wavelength of approximately 1550nm), using a simple two-stage interferometer setup. A remarkable synthesis accuracy and high energetic efficiency have been achieved for all these pulse re-shaping operations.
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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) | 0.001 | 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".