A New Momentum-Integrated Muon Tomography Imaging Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
For decades, the application of muon tomography to spent nuclear fuel (SNF) cask imaging has been theoretically evaluated and experimentally verified by many research groups around the world, including Los Alamos National Laboratory in the United States, Canadian Nuclear Laboratory in Canada, the National Institute for Nuclear Physics in Italy, and Toshiba in Japan. Although monitoring of SNF using cosmic ray muons has attracted significant attention as a promising nontraditional nondestructive radiographic technique, the wide application of muon tomography is often limited because of the natural low cosmic ray muon flux at sea level: 100 m-2min-1sr-1. Recent studies suggest measuring muon momentum in muon scattering tomography (MST) applications to address this challenge. Some techniques have been discussed; however, an imaging algorithm for momentum-coupled MST had not been developed. This paper presents a new imaging algorithm for MST which integrates muon scattering angle and momentum in a single M-value. To develop a relationship between muon momentum and scattering angle distribution, various material samples (Al, Fe, Pb, and U) were thoroughly investigated using a Monte Carlo particle transport code GEANT4 simulation. Reconstructed images of an SNF cask using the new algorithm are presented herein to demonstrate the benefit of measuring muon momentum in MST. In this analysis a missing fuel assembly (FA) was located in the dry storage cask.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it