Multiple-scattering muography of a thick-walled spent-fuel cask: A high-statistics Geant4 simulation
Bibliographic record
Abstract
Multiple-scattering muography is a passive, non-destructive technique for assessing spent-fuel dry-storage casks. A key challenge is to quantify where and how muons interact inside such a cask, for instance, the CASTOR® V/19, and to translate those interactions into practical guidance for detector layout and reconstruction algorithms. A detailed Geant4 model of a fully loaded CASTOR V/19 cask with four ideal tracking planes was simulated with 50×106 atmospheric-muon tracks. Three analysis observables were defined: Total material crossings Nhit, hard-scatter count Nint, and the integrated root mean square (rms) scattering angle Δθrms. These metrics build an interaction taxonomy allowing to connect muon tracks to detector and algorithm requirements. We find that of the incident muons, 36% traversed the shielding, yielding 18.4×106 reconstructed trajectories. Each transmitted track intersected on average 15.5 (±9.4) fuel rods and 2.2 (±1.1) fuel assemblies, generating more than 11×107 fuel-rod crossings for statistical analysis. Bottom-exiting tracks, ≈87% of which interacted with fuel, carried the highest information content. The interaction taxonomy links regimes of Nint to a reconstruction algorithm choice, and it informs detector placement. These results provide a quantitative baseline for optimizing detector geometry and reconstruction strategy in CASTOR V/19 muography.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".