Plasma density shaping for attosecond electron bunch generation
Bibliographic record
Abstract
High energy attosecond electron bunches from the laser-plasma wakefield accelerator (LWFA) are potentially useful sources of ultra-short duration X-rays pulses, which can be used for ultrafast imaging of electron motion in biological and physical systems. Electron injection in the LWFA depends on the plasma density and gradient, and the laser intensity. Recent research has shown that injection of attosecond electron bunches is possible using a short plasma density ramp. For controlled injection it is necessary to keep both the laser intensity and background plasma density constant, but set to just below the threshold for injection. This ensures that injection is only triggered by an imposed density perturbation; the peak density should also not exceed the threshold for injection. A density gradient that only persists over a short range can lead to the injection of femtosecond duration bunches, which are then Lorentz contracted to attoseconds on injection. We consider an example of a sin2 shaped modulation where the gradient varies until the downward slope exceeds the threshold for injection and then reduces subsequently to prevent any further injection. The persistence above the threshold determines the injected bunch length, which can be varied. We consider several designs of plasma media including density perturbations formed by shaped Laval nozzles and present an experimental and theoretical study of the modulated media suitable for producing attosecond-duration electron bunches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".