Time-of-flight methodologies with large-area diamond detectors for ion characterization in laser-driven experiments
Bibliographic record
Abstract
Abstract The time-of-flight technique coupled with semiconductor detectors is a powerful instrument to provide real-time characterization of ions accelerated because of laser–matter interactions. Nevertheless, the presence of strong electromagnetic pulses (EMPs) generated during the interactions can severely hinder its employment. For this reason, the diagnostic system must be designed to have high EMP shielding. Here we present a new advanced prototype of detector, developed at ENEA-Centro Ricerche Frascati (Italy), with a large-area (15 mm × 15 mm) polycrystalline diamond sensor having 150 μm thickness. The tailored detector design and testing ensure high sensitivity and, thanks to the fast temporal response, high-energy resolution of the reconstructed ion spectrum. The detector was offline calibrated and then successfully tested during an experimental campaign carried out at the PHELIX laser facility ( ${E}_L\sim$ 100 J, ${\tau}_L = 750$ fs, ${I}_L\sim \left(1{-}2.5\right)\times {10}^{19}$ W/cm2) at GSI (Germany). The high rejection to EMP fields was demonstrated and suitable calibrated spectra of the accelerated protons were obtained.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".