Short- and long-term gain dynamics in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mi mathvariant="normal">N</mml:mi><mml:mn>2</mml:mn><mml:mo>+</mml:mo></mml:msubsup></mml:math> air lasing
Bibliographic record
Abstract
Air lasing in the nitrogen molecular ion is not well understood because the complex physics responsible for gain is interwoven with pulse propagation in an extreme environment. Here we use a short gas jet to limit the interaction length, thereby removing the propagation effects. We report on several mechanisms that contribute to the decay of gain in different conditions, and experimentally isolate two decay timescales: the decay of long-term gain due to collisional state mixing, and short-term gain that cannot be explained by population inversion. To test the former, we control the inelastic electron scattering rate by varying the gas concentration while keeping the propagation length fixed, and predict the change of the decay using a model of collisional state mixing. We show that the same mechanism causes the decay of rotational wave packets in the states of the ion. Finally, we simulate the complex modulations of gain due to rotational wave packets and the propagation of the probe pulse through the evolving rotationally excited and inverted medium.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".