Evolution of non-Gaussian electron bunches in ultrafast electron diffraction experiments: Comparison to analytic model
Bibliographic record
Abstract
We present a comparison of a three-dimensional analytic Gaussian (AG) model of electron bunch propagation with numerical simulations of quasi- and non-Gaussian distributions. Quasi- and non-Gaussian distributions are a good representation of electron bunches used in ultrafast electron diffraction (UED) experiments, and we show that the AG model is successful in predicting the evolution of such freely propagating bunches. The bunch parameters in our comparisons are the bunch size, the total momentum spread, and the local momentum spread. In the case of the local momentum spread, which is related to the bunch coherence length, we compare the predictions of the AG model with three methods for calculating the local momentum spread from numerical data. This comparison also highlights the difficulties of calculating the evolution of the local momentum parameter from N-body simulations. The AG model shows good agreement with N-body simulations of different distributions for all the bunch parameters and is therefore a convenient tool for refining the UED experimental design.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| 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".