A geometry-aware framework for electromagnetic fields in GATE 10
Objective. An increasing number of medical-physics applications require the modelling of charged-particle transport in electromagnetic (EM) fields. We aim to provide GATE 10 users with a flexible mechanism for defining such fields in Monte Carlo simulations while preserving the native Geant4 particle-transport machinery. Approach. Our framework separates the handling of coordinates from EM field evaluation. Fields are attached to geometry volumes, defined in their local frame, and represented by analytic expressions, field maps, or arbitrary Python functions. They automatically follow the position and orientation of these volumes, including in nested and dynamic geometries. We validated the framework against native Geant4 transport and tested coordinate transformations, nested volumes, and dynamic geometry. We also compared the computational cost of the three field representations. Main results. The framework reproduced the native Geant4 trajectories exactly for all tested field types with direct Geant4 counterparts. Coordinate transformations introduced only floating-point-level differences, while simulations with dynamically changing geometry reproduced the equivalent static configurations exactly. All three representations produced equivalent transport. Mapped fields added at most 18% to the single-threaded runtime of analytic fields, while Python functions required 6.9 times as much. The compiled representations scaled with the number of threads, whereas Python functions became slower as threads were added. Significance. The framework adds flexible, geometry-aware EM field definitions to GATE 10 without modifying the underlying Geant4 transport machinery, and is part of the open-source GATE 10 codebase. It also provides a basis for extending GATE 10 with additional field representations in the future.