Speakers
Description
High-precision calorimeter simulation at current and future colliders puts growing demands on computing resources, motivating ML-based alternatives to traditional Monte Carlo tools such as Geant4. In practice, generative models based on flow matching and diffusion have become de facto standards for high-dimensional fast calorimeter simulation, thanks to their excellent fidelity and strong track record across ML research and industry applications. However, this typically comes with longer generation time, since high precision shower modeling needs more function evaluations during inference. In addition, many current proposals introduce separate networks to correct or constrain high-level observables, such that the overall pipeline is no longer strictly end-to-end.
In this study, we introduce three ingredients that together form a unified framework aimed at a better balance between generation speed, shower quality, and physics fidelity. First, the method employs an average velocity field integrator that allows sampling in one or a few steps, instead of the many evaluations required by conventional solvers. Second, a learned generative prior based on an isotropic Gaussian in shower space is constructed from the shower rather than from a random noise distribution, which leads to faster convergence and improved shower quality. Third, physics-guided loss terms provide useful inductive bias to constrain key observables during training. These elements act purely as training-time regularizers, so inference remains strictly end-to-end with no additional cost. With only one or a few evaluations, we achieve shower quality competitive with state-of-the-art flow and diffusion models that use O(100) solver steps, demonstrated on several public high granularity calorimeter datasets. The resulting model captures detailed inter-layer shower structure consistent with the underlying physics, which has been challenging for many previous approaches, providing a strong candidate for future fast simulation workflows.