The propagation of powerful femtosecond laser pulses in opticalmedia: physics, applications, and new challenges
Bibliographic record
Abstract
When a powerful femtosecond laser pulse propagates in an optical medium, self-focusing occurs. Normally, it is the most powerful part (slice) of the pulse that self-focuses first during its propagation. Self-focusing is balanced by the creation of plasma in the self-focal volume, which defocuses the pulse. This balance leads to a limitation of the peak intensity (intensity clamping). The series of self-foci from different slices of the front part of the pulse give rise to the perception of a so-called filament. The back part of the pulse undergoes self-phase modulation and self-steepening resulting in a strong spectral broadening. The final pulse is a white-light laser pulse (supercontinuum). The physics of such (long distance) filamentation and the self-transformation process are reviewed both in air and in condensed matters. The self-transformation leads to a shorter pulse and is currently being studied for efficient pulse compression to the single and (or) few-cycle level. The efficient generation of a third harmonic in the filament is due to a new phenomenon called self-phase locking. The potential applications in atmospheric sensing and lightning control will be briefly discussed. The capability of melting glass leading to index change will be underlined. The paper will end with an outlook into the future of the field. PACS Nos.: 42.65, 42.65Jx, 42.25, 42.79Qx
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".