Speaker
Description
Deep generative surrogates can cut the computing cost of detailed Geant4 calorimeter simulation, but they remain tied to the detector they were trained on: each new geometry typically demands a large in-domain dataset. We present two studies that reduce this dependence for point cloud generative models.
In the first, a CaloClouds-based diffusion model pre-trained on ILD photon showers transfers to electron showers in the cylindrical CaloChallenge geometry without re-voxelisation. Fine-tuning on only 100 target showers improves the geometric mean of the Wasserstein distances to Geant4 by about 50% over training from scratch, and bias-only adaptation stays competitive while updating 17% of the parameters.
The second study scales to multi-geometry pre-training with AllShowers, a transformer flow-matching model. It compares two pre-training pools: a synthetic family of $10^4$ box calorimeters and a set of realistic detectors. On the held-out FCC-ee ALLEGRO calorimeter, with $10^3$ target showers for fine-tuning, the two priors reduce the aggregated sliced Wasserstein distance to Geant4 by factors of 5.2 (synthetic) and 8.0 (realistic) relative to training from scratch. The purely synthetic prior matches the realistic one: geometric diversity alone is enough to pre-train a transferable shower model. The measured pre-training cost amortises within a few fine-tunings, making pre-trained surrogates a practical route to fast simulation for future detectors.