Parametric investigation of laser wakefield acceleration versus F-number of focusing optics
Bibliographic record
Abstract
Laser wakefield acceleration is a growing area of research with the promise of generating high energy, low divergence, and short duration electron bunches from tabletop scale accelerators. To date, electron beams with maximum energy of 1 GeV with 2.5% energy spread have been generated using a 3cm plasma channel [1] . However in order to advance the maximum energy of electron beams beyond this limit, better understanding of the physics and effect of different parameters on the interaction are essential. In this paper we report on our parametric studies of wakefield electron acceleration using the 10TW chirped pulse amplified laser system at the Advanced Laser Light Source (ALLS), Montreal. Laser pulses with energies of ~210 mJ at 33fs were focused using a short (f/6) and a long focal length (f/12) off axis parabola onto 2mm supersonic helium and nitrogen gas jets at different pressures. Nitrogen with electron densities of up to 2×10 20 cm -3 and helium densities up to 5×10 19 were used. Beams with energies of tens of MeV were observed using the short focal length parabola and beams with energies of several MeV were observed using the long focal length parabola. We also found that electron beams are more easily generated with higher levels of prepulse, consistent with previous reports of prepulse generated guiding channels in the plasma[5].
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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 | 0.000 | 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".