ONE-SIDED MUON TOMOGRAPHY—A PORTABLE METHOD FOR IMAGING CRITICAL INFRASTRUCTURE WITH A SINGLE MUON DETECTOR
Bibliographic record
Abstract
High-energy muons generated from cosmic-ray particle showers have been shown to exhibit properties ideal for imaging the interior of large and dense structures. This paper explores the possibility of using a single portable muon detector in conjunction with image reconstruction methods used in nuclear medicine to reconstruct a 3-D image of the interior of critical infrastructure such as the Zero-Energy Deuterium (ZED-2) research reactor at Canadian Nuclear Laboratories’ Chalk River site. The ZED-2 reactor core and muon detector arrangement are modeled in GEANT4 and Monte Carlo measurements of the resultant muon throughput and angular distribution at several angles of rotation around the reactor are generated. Statistical analysis is then performed on these measurements based on the well-defined flux and angular distribution of muons expected near the surface of the earth. The results of this analysis are shown to produce reconstructed images of the spatial distribution of nuclear fuel within the core for multiple fuel configurations. This one-sided tomography concept is a possible candidate for examining the internal structure of larger critical facilities, for example the Fukushima Daiichi power plant, where the integrity of the containment infrastructure and the location of the reactor fuel is unknown.
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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) | 0.001 | 0.001 |
| 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) | 0.006 | 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".