TY  - EJOUR
AU  - Kasieczka, Gregor
AU  - Gaede, Frank
AU  - Valente, Lorenzo
TI  - Cross-Geometry Transfer Learning in Fast Electromagnetic Shower Simulation
IS  - arXiv:2512.00187
CY  - arXive
M1  - PUBDB-2025-05215
M1  - arXiv:2512.00187
PY  - 2025
AB  - 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 framework 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 detector, our approach handles new configurations without revoxelizing showers for each geometry. On the CaloChallenge dataset, transfer learning with only 100 target-domain samples achieves a 44
LB  - PUB:(DE-HGF)25
UR  - https://bib-pubdb1.desy.de/record/641834
ER  -