Electron Filament Structures of Injected Electrons in LWFA
Bibliographic record
Abstract
We observe the formation of thread-like elongated electron structures, referred to as electron filaments, during the self-injection in the bubble regime of laser wakefield acceleration (LWFA) using 2-D particle in cell (PIC) simulations, for no plasma density upramp preceding the laser pulse. At relatively low plasma densities, around$ {\mathrm {e18~\text {c}\text {m} }}^{-3}$, the self-injected electrons continue to move on separate trajectories inside the bubble creating well-defined electron filaments when a large bubble closes its rear edges from behind, making an angle larger than 180° measured from the interior of the bubble. At higher plasma densities, above 2.5 × 1018cm−3, the bubble closes at its rear with the edges making a smaller angle than 180°, resulting in a different injection dynamics. We examine these trajectories using Hamiltonian mechanics and how they impact the betatron oscillations in these cases. The results indicate that higher plasma densities result in smaller betatron oscillation amplitudes and larger wiggler strength parameter due to the stronger accelerating fields in the bubble, which accelerate the electrons to higher energies, giving higher Lorentz factors. We also show that by introducing a linear density upramp at the beam entrance, the bubble’s geometry is modified, thereby influencing the betatron oscillations and potentially tuning the acceleration process.
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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 | 0.000 |
| 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".