Femtosecond pulse shaping in the mid-infrared generated by difference-frequency mixing: a simulation and experiment
Bibliographic record
Abstract
We have examined phase- and amplitude-modulated femtosecond laser pulses in the mid-infrared (MIR) region (3-10 μm) generated by difference-frequency mixing both theoretically and experimentally. Transfer of the pulse shape from near infrared to MIR by a difference-frequency process was evaluated in detail for various spectra, linear chirps, phases, and optical delays of two pulses before the different frequency was compared and with experimentally obtained MIR shapes. In the experiment, the signal pulse of an optical parametric amplifier was shaped with an acousto-optic programmable dispersive filter and mixed in an AgGaS2 crystal with the idler pulse that was temporally stretched by passing it through a dispersion block to generate a shaped MIR pulse. The agreement between the theory and experiment was reasonable despite the complicated experimental procedure. It was demonstrated that the resultant MIR pulse shape could be completely different from the pulse shape before the difference-frequency generation. However, it is possible to reproduce any shape of MIR pulses by predicting the pulse shape using the present theoretical framework. This will allow us to manipulate rovibrational wave packets of real molecules for practical applications.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".