Speaker
Description
Muon scatter imaging is based on detectors giving information on the incoming and outgoing muon positions and angles, and extracted total scattering angle, with no direct information on the muon track between the detectors. That is, we are trying to recover rich information from a low information environment. There are many methods of reconstructing each muon's path, including straight line, point of closest approach, splining, and even iterative methods.
In our current work we examine the fidelity of reconstruction methods using Geant4 Monte Carlo simulations as a reasonable representation of reality, or the rich information to be reconstructed. In the simulations we have the entire trajectory for each muon and we compare the residuals - the difference - between common track reconstruction results and the detailed path simulations. A priori knowledge is used to guide reconstructions, such as using known geometries and running straight line paths through air portions of a region imaged, such as the air outside of a spent nuclear fuel cask. Other considerations include energy loss effects, as the muons tend to scatter to larger angles as they lose energy (e.g., [1,2]). Path curvature change may be considered by combining different track reconstruction methods such as straight line and spline with a moving weighting. While experimentally we don't know the muon energy, we do know the general behavior that high total scattering angles are associated with lower energies, which can guide us in parsing the data into subsets for analysis.
Residuals between full path simulations and reconstructions based on the entrance and exit positions and angles are compared for different reconstruction methods to find an optimal path reconstruction. Image reconstructions of Geant4 simulated data with the MC-10 spent nuclear fuel cask at Idaho National Laboratory were performed to compare the imaging outcomes of the different reconstruction methods including previous image reconstruction [3].
- R. Ughade, S. Chatzidakis, J. Appl. Phys. 138, 064909 (2025)
- S. Chatzidakis, Z. Liu, J.P. Hayward, J.M. Scaglione, J. Appl. Phys. 123, 124903 (2018)
- J.J. Valencia, J.W. Sperow, J.M. Durham, C.L. Morris, A.G. Osborne, D. Poulson, A.A. Hecht, J. Appl. Phys. 138, 184502 (2025)