SNM detection by active muon interrogation
Bibliographic record
Abstract
Muons are charged particles with mass between the electron and proton and can be produced indirectly through pion decay by interaction of a charged-particle beam with a target. There are several distinct features of the muon interaction with matter attractive as a probe for detection of SNM at moderate ranges. These include muon penetration of virtually any amount of material without significant nuclear interaction until stopped by ionization loss in a short distance. When stopped, high-energy penetrating x-rays (in the range of 6 MeV for uranium,) unique to isotopic composition are emitted in the capture process. The subsequent interaction with the nucleus produces additional radiation useful in assessing SNM presence. A focused muon beam can be transported through the atmosphere, at a range limited mainly by beam-size growth through scattering. A muonbeam intensity of > 10{sup 9} /second is required for efficient interrogation and, as in any other technique, dose limits are to be respected. To produce sufficient muons a high-energy (threshold {approx}140 MeV) high-intensity (<1 mA) proton or electron beam is needed implying the use of a linear accelerator to bombard a refractory target. The muon yield is fractionally small, with large angle and energy dispersion, so that efficient collection is necessary in all dimensions of phase space. To accomplish this Los Alamos has proposed a magnetic collection system followed by a unique linear accelerator that provides the requisite phase-space bunching and allows an energy sweep to successively stop muons throughout a large structure such as a sea-going vessel. A possible maritime application would entail fitting the high-gradient accelerators on a large ship with a helicopter-borne detection system. We will describe our experimental results for muon effects and particle collection along with our current design and program for a muon detection system.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".