I work on making one generative shower model reusable across calorimeter geometries. Pre-trained on several detectors, it adapts to a new one with two orders of magnitude fewer Geant4 showers than a model trained from scratch.
Website · ORCID · Hugging Face · LinkedIn
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Transferable Fast Calorimeter Shower Generation via Multi-Geometry Pre-training, JINST 21 (2026) P10041.
Code: AllShowers and PointCountFM, weights on Hugging Face. -
Cross-Geometry Transfer Learning in Fast Electromagnetic Shower Simulation, JINST 21 (2026) P07037.
Code: CaloTransfer. -
CaloClouds3: Ultra-fast Geometry-Independent Highly-Granular Calorimeter Simulation, JINST 21 (2026) P03018.
Full list on INSPIRE.
- multi-calorimeter-dataset: Geant4/DD4hep production of the SimpleBox and LEMURS point cloud shower datasets, from simulation to training-ready HDF5, data on Hugging Face.
- ddFastSim: DD4hep fast simulation framework, I develop the mesh sensitive detector extensions used for these datasets (PR #2).


