Ponderomotive-expulsion: toward creating an electron-free volume
Bibliographic record
Abstract
We describe a demonstration of a prototype approach to clear the laser focal volume of free electrons and disable their atomic and molecular sources. Employing two temporally separated, copropagating pulses, we exploited a pump-probe setup in our experiment. The pump ionized a low-density gas and expelled free and nascent electrons from its focal volume. The probe, traversing the same focal volume, expelled any remaining free and probe-induced nascent electrons. We gauged the effectiveness of the approach by capturing the spatial distribution of ejected electrons with image plates while we varied the relative intensity and time delay between the pump and probe. When we injected the pump 300 fs before the probe, we found the electron spatial distribution significantly altered and the yield suppressed, proving ponderomotive expulsion works. However, the yield was enhanced when we set the temporal spacing between the pump and probe to 150 fs. Simulations show the enhancement is due to Airy rings of the focused pump expelling electrons inward toward the propagation axis. Our results show that the complete removal of focal-volume electrons was inhibited by spatial overlap fluctuations and the stronger probe generating ionization outside the cleared-volume of the pump. We discuss ways to mitigate these impediments and propose alternate two-beam arrangements to achieve more efficient focal-volume clearing.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".