Ultrafast adiabatic frequency conversion
Bibliographic record
Abstract
Abstract Ultrafast adiabatic frequency conversion is a powerful method, capable of efficiently and coherently transfering ultrashort pulses between different spectral ranges, e.g. from near-infrared to mid-infrared, visible or ultra-violet. This is highly desirable in research fields that are currently limited by available ultrafast laser sources, e.g. attosecond science, strong-field physics, high-harmonic generation spectroscopy and multidimensional mid-infrared spectroscopy. Over the past decade, adiabatic frequency conversion has substantially evolved. Initially applied to quasi-monochromatic, undepleted pump interactions, it has been generalized to include ultrashort, broadband, fully-nonlinear dynamics. Through significant theoretical development and experimental demonstrations, it has delivered new capabilities and superior performance in terms of bandwidth, efficiency and robustness, as compared to other frequency conversion techniques. This article introduces the concept of adiabatic nonlinear frequency conversion, reviews its theoretical foundations, presents significant milestones and highlights contemporary ultrafast applications that may, or already do, benefit from utilizing this method.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".