Linear Characterization of Optical Pulses With Durations Ranging From the Picosecond to the Nanosecond Regime Using Ultrafast Photonic Differentiation
Bibliographic record
Abstract
<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In this paper, we extend the recently introduced linear technique for temporal phase reconstruction using optical ultrafast differentiation (PROUD) to achieve full characterization of ultrashort optical pulses with durations down to the picosecond regime using a well-characterized temporal stretcher (e.g., dispersive optical fiber). The proposed method is experimentally demonstrated by precisely characterizing the amplitude and phase temporal profiles of microwatt-power picosecond pulses ranging from 4 to 20 ps with both continuous and discrete temporal phase variations. Using this simple mechanism, the same PROUD setup can be used to characterize optical pulses with durations ranging from the picosecond to the nanosecond regime. We provide a comprehensive mathematical analysis of this general PROUD technique: we evaluate in detail the influence of the key specifications (e.g., different sources of noise) of the used components and instruments, namely, optical differentiator, linear temporal stretcher, and time-domain intensity test equipment, on the performance of the PROUD measurement system, particularly in terms of phase sensitivity in the optical pulse characterization. </para>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".