Simulation and Measurements of Collimator Effects in Proton and Neutron Radiation Testing for Single-Event Effects
Bibliographic record
Abstract
During single-event effect (SEE) testing of electronic devices with collimated proton and neutron beams at accelerator facilities, support equipment is often located in close proximity to the beam. Background radiation in the area outside the nominal beam spot is created when the beam interacts with components of the beamline such as shielding, collimators, and monitoring devices, or when the beam interacts with high density elements of the devices under test. This background radiation can cause unforeseen and undesirable issues in the support equipment. At the TRIUMF Proton and Neutron Irradiation (PIF & NIF) facilities, an SRAM-based dosimeter was used to measure proton and neutron beam profiles. In some of these measurements, the beamline configuration was designed to enhance the background level. To better understand the processes that give rise to this background, and how it changes with the measurement position, beam energy, and beam type, these measurements are compared to FLUKA simulations of two of TRIUMF's beamlines. The simulated and measured beam profiles show a good general agreement over a range much wider than the beam spot size. For proton beams, the background is around 1% of the central beam intensity and the relative importance of neutron and slit-scattered protons changes with the distance from the final collimator. For the neutron beams studied, the background is dominated by neutrons and is around 30% of the central beam intensity whether or not collimators are used to manipulate the size of the beam spot.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".