Spectro-Temporal Characterization of Tunable Supercontinuum Using X-FROG Measurements
Bibliographic record
Abstract
Optical wave-packet control and optimization is highly sought-after in numerous demanding applications. Recent advances have shown the potential of programable photonic integrated chips (PICs) [1] for enabling new optical functionalities. For instance, programmable delay lines (PDL) can be used for autonomous picosecond pulse shaping [2] as well as supercontinuum (SC) spectral shaping [3]. Recent work also demonstrated the potential of such approaches for gaining access to advanced spectro-temporal control [4] which would directly benefit applications such as nonlinear imaging and multiphoton microscopy [5]. Here, we demonstrate the spectro-temporal characterization and adaptive adjustment of broadband supercontinua (SC). The experimental setup shown in Fig. 1(a) comprises a femtosecond laser at 1550 nm, injected into an on-chip PDL [2]–[4] and used to generate a train of femtosecond pulses with a tunable pattern. This train of pulses is then amplified and sent into a highly nonlinear fiber (HNLF) to experience nonlinear spectral broadening. The broadband output SC spectrum is finally characterized via an X-FROG [6] setup that mixes the SC with an asynchronous femtosecond laser source (at 1040 nm) resulting in a sum-frequency generation (SFG) signal measured with a fast spectrometer. Here, the two lasers exhibit different yet stable repetition rates (i.e. locked but asynchronous), such that there is a constant frequency detuning yielding a fast scanning effect for the acquisition of the X-FROG spectrograms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".