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Non-intrusive Monitoring of Sealed Microreactor Cores Using Physics-Informed Muon Scattering Tomography With Momentum Measurements

Reshma Ughade, Stylianos Chatzidakis

Abstract

Next-generation microreactors enable remote deployment and semi-autonomous operation, but compact, sealed, heterogeneous cores limit conventional safeguard approaches that rely on access and bulk accountancy. Limited inspection access and complex internal geometry reduce sensitivity to localized anomalies such as missing fuel. Here we demonstrate missing-fuel detection in microreactor scale geometries using muon scattering tomography under realistic cosmic-ray conditions. We introduce $μ$TRec, a physics-informed framework that reconstructs event-level curved muon trajectories by combining a Gaussian multiple Coulomb scattering model with Bayesian updating, then maps scattering density through voxel wise M-values for core integrity verification. We evaluate a representative hexagonal core containing 61 fuel flakes with embedded control drums and shutdown rods, using both idealized 5 GeV muons and zenith-angle-dependent 0-60 GeV cosmic-ray spectra. A single missing fuel flake is detected with $3\times 10^{6}$ muons at 50 mm voxel resolution. Incorporating per-muon momentum further increases detectability by up to 149.85% for laser-driven sources and 105.11% for cosmic-ray sources relative to momentum-agnostic reconstruction. The approach remains robust under practical detector limits, with only an 8.88% reduction in detectability for 10 mm spatial resolution and 10% energy resolution. Compared with PoCA, $μ$TRec delivers 326.13% to 392.14% higher detectability at equal muon counts, enabling faster defect identification.

Non-intrusive Monitoring of Sealed Microreactor Cores Using Physics-Informed Muon Scattering Tomography With Momentum Measurements

Abstract

Next-generation microreactors enable remote deployment and semi-autonomous operation, but compact, sealed, heterogeneous cores limit conventional safeguard approaches that rely on access and bulk accountancy. Limited inspection access and complex internal geometry reduce sensitivity to localized anomalies such as missing fuel. Here we demonstrate missing-fuel detection in microreactor scale geometries using muon scattering tomography under realistic cosmic-ray conditions. We introduce TRec, a physics-informed framework that reconstructs event-level curved muon trajectories by combining a Gaussian multiple Coulomb scattering model with Bayesian updating, then maps scattering density through voxel wise M-values for core integrity verification. We evaluate a representative hexagonal core containing 61 fuel flakes with embedded control drums and shutdown rods, using both idealized 5 GeV muons and zenith-angle-dependent 0-60 GeV cosmic-ray spectra. A single missing fuel flake is detected with muons at 50 mm voxel resolution. Incorporating per-muon momentum further increases detectability by up to 149.85% for laser-driven sources and 105.11% for cosmic-ray sources relative to momentum-agnostic reconstruction. The approach remains robust under practical detector limits, with only an 8.88% reduction in detectability for 10 mm spatial resolution and 10% energy resolution. Compared with PoCA, TRec delivers 326.13% to 392.14% higher detectability at equal muon counts, enabling faster defect identification.
Paper Structure (19 sections, 15 equations, 11 figures, 6 tables)

This paper contains 19 sections, 15 equations, 11 figures, 6 tables.

Figures (11)

  • Figure 1: EMD microreactor: (a) radial and axial diagram with labeled core components PRICE2023112709 and (b) basic layout with enlarged fuel flake PRICE2024134.
  • Figure 2: Simulated cosmic-ray muon source characteristics used in GEANT4: (a) energy and (b) angular (zenith) distributions chatzidakis_geant4-matlab_2015.
  • Figure 3: Measurement setup of EMD microreactor (grey) modelled in GEANT4 for muon entry/exit tracking using four detectors (green).
  • Figure 4: Illustration of multiple Coulomb scattering in the $y$--$z$ plane ughade_trec_2025.
  • Figure 5: Pseudocode for the momentum-informed $\mu$TRec algorithm. The no-momentum version is detailed in Ref. ughade_trec_2025.
  • ...and 6 more figures