An analysis of pressurized heavy water reactor fuel for nuclear safeguards applications using muon scattering tomography
Bibliographic record
Abstract
Muon Scattering Tomography (MST) relies on multiple Coulomb scattering (MCS) of high energy cosmic ray muons in matter to reconstruct 2D and 3D images of targets of interest. Targets such as nuclear fuel and containers for spent nuclear fuel are difficult or impossible to image using conventional X-ray techniques due to the presence of shielding and the high density/high-Z nature of the materials involved. MST is a modern non-destructive technique using naturally occurring radiation to assay such materials and geometries. This technique is particularly well suited for applications in spent nuclear fuel management and nuclear non-proliferation since scanning time constraints are more relaxed compared to applications of border security. The Cosmic Ray Inspection and Passive Tomography (CRIPT) detector and associated Geant4 simulation were used for this investigation. A unique capability of CRIPT is the reconstruction of the muon momentum with a spectrometer. Presented here are measurements of un-irradiated Pressurised Heavy Water Reactor (PHWR) fuel bundles in contrast with a lead stack, steel-loaded, and voided fuel bundle analogues ("fakes"). The results presented here show that MST can effectively be used to verify the contents of spent fuel storage containers and is the first experimental analysis of PHWR fuel in contrast with "fake" bundles in a safeguards context. In cases where statistics (exposure times) are not limited, ceramic UO 2 , Pb, and weighted/un-weighted "fake" fuel bundles can be both statistically and qualitatively distinguished which serves to demonstrate the efficacy of MST for nuclear safeguards/non-proliferation applications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".