High harmonic generation with a spatially filtered optical parametric amplifier
Bibliographic record
Abstract
Numerous applications of high harmonic generation (HHG), such as attosecond pulse synthesis, depend on the ability to increase the electron recollision energy, which is a quadratic function of the driver wavelength. High-energy infrared pulses obtained from an optical parametric amplifier (OPA) are thus attractive for driving the HHG process, thereby offering the opportunity to yield shorter attosecond pulses. However, the increase in driver wavelength is often outweighed by the poor spatial quality of the OPA source. In this paper, we demonstrate that HHG using OPA signal pulses is significantly improved by spatial filtering in a hollow-core fibre prior to focusing in the gas target in comparison with the unfiltered case. Ion yield measurements in combination with beam profile monitoring in the far field enabled control over the interaction volume. For similar interaction volumes, we observe that with less than half the energy per pulse, the HHG yield can increase by one order of magnitude with spatial filtering. The comparison between the harmonic yields in argon and krypton, and their respective dependence on the peak laser intensity, provide experimental evidence that strongly suggests that the enhancement is due to improved phase matching.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".