High energy redshifted and enhanced spectral broadening by molecular alignment
Bibliographic record
Abstract
We demonstrate an efficient approach for enhancing the spectral broadening of long laser pulses and for efficient frequency redshifting by exploiting the intrinsic temporal properties of molecular alignment inside a gas-filled hollow-core fiber (HCF). We find that laser-induced alignment with durations comparable to the characteristic rotational time scale <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msubsup><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi>T</mml:mi></mml:mrow><mml:mrow class="MJX-TeXAtom-ORD"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="normal">R</mml:mi><mml:mi mathvariant="normal">o</mml:mi><mml:mi mathvariant="normal">t</mml:mi></mml:mrow></mml:mrow><mml:mrow class="MJX-TeXAtom-ORD"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="normal">A</mml:mi><mml:mi mathvariant="normal">l</mml:mi><mml:mi mathvariant="normal">i</mml:mi><mml:mi mathvariant="normal">g</mml:mi><mml:mi mathvariant="normal">n</mml:mi></mml:mrow></mml:mrow></mml:msubsup></mml:math> enhances the efficiency of redshifted spectral broadening compared to noble gases. The applicability of this approach to Yb lasers with (few hundred femtoseconds) long pulse duration is illustrated, for which efficient broadening based on conventional Kerr nonlinearity is challenging to achieve. Furthermore, this approach proposes a practical solution for high energy broadband long-wavelength light sources, and it is attractive for many strong field applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".