Nonparametric Dense-Object Detection Algorithm for Applications of Cosmic-Ray Muon Tomography
Bibliographic record
Abstract
We present an algorithm that utilizes data generated by cosmic-ray muon-scattering tomography for passive nondestructive detection of dense objects. Our clustering-based approach uses a nonparametric statistical test based on a reference case to determine the presence of high-density high-$Z$ material, such as illicit nuclear material hidden inside a shipping canister. The algorithm outputs a single decision value for the absence or presence of illicit material without the need to perform a detailed visual tomographic reconstruction and/or the need to rely on human interpretation, in contrast to many other muon-based imaging techniques. The performance of the algorithm is demonstrated using experimental data obtained with the Cosmic-Ray Inspection and Passive Tomography detector from two setups consisting of a lead flask, a 55-gallon drum filled with sand, and 2 kg of metallic depleted uranium (DU). The results of these experiments illustrate that the high-density lead flask can be automatically distinguished from the background, with an area under the receiver-operating-characteristic curve (AUC) of 0.90 using less than 90 s of data; the lead flask with 2 kg of DU inside can be detected within the 55-gallon drum containing sand in under 5 min with an AUC of 0.91. Our experimental results illustrate the efficacy of this algorithm for the identification of dense objects using reference background measurements. Practical applications of this algorithm are discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".