Cross-geometry transfer learning in fast electromagnetic shower simulation

Abstract Accurate particle shower simulation remains a critical computational bottleneck for high-energy physics. Traditional Monte Carlo methods, such as Geant4 , are computationally prohibitive, while existing machine learning surrogates are tied to specific detector geometries and require complete retraining for each design change or alternative detector. We present a transfer learning methodology for generative calorimeter simulation models that enables adaptation across diverse geometries with high data efficiency. Using point cloud representations and pre-training on the International Large Detector, our approach handles new configurations without re-voxelizing showers for each geometry. On the CaloChallenge dataset, transfer learning with only 100 target-domain samples achieves a 51% improvement on the geometric mean of Wasserstein distance over training from scratch. Parameter-efficient fine-tuning with bias-only adaptation achieves competitive performance while updating only 17% of model parameters. Our analysis provides insight into adaptation mechanisms for particle shower development, establishing a baseline for future progress of point cloud approaches in calorimeter simulation.