Thomson parabola and time-of-flight detector cross-calibration methodology on the ALLS 100 TW laser-driven ion acceleration beamline
Bibliographic record
Abstract
We report on the cross-calibration of Thomson Parabola (TP) and Time-of-Flight (TOF) detectors as particle diagnostics, implemented on the most recent setup of the ALLS 100 TW laser-driven ion acceleration beamline. The Microchannel Plate (MCP) used for particle detection in the TP spectrometer has been calibrated in intensity on the tandem linear accelerator at the Université de Montréal. The experimental data points of the scaling factor were obtained by performing a pixel cluster analysis of single proton impacts on the MCP. A semi-empirical model was extrapolated and fitted to the data to apply the calibration also to higher kinetic energies and to extend it to other ion species. Two TOF lines using diamond detectors, placed at +6° and −9° with respect to the target-normal axis, were benchmarked against the TP spectrometer measurements to determine the field integrals related to its electric and magnetic dispersions. The mean integral proton numbers obtained on the beamline were about 4.1 × 1011 protons/sr with a standard deviation of 15% in the central section of the spectrum around 3 MeV, hence witnessing the high repeatability of the proton bunch generation. The mean maximum energy was of 7.3 ± 0.5 MeV, well in agreement with similar other 100 TW-scale laser facilities, with the best shots reaching 9 MeV and nearly 1012 protons/sr. The used particle diagnostics are compatible with the development of a high-repetition rate targetry due to their fast online readout and are therefore a crucial step in the automation of any beamline.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".