Inductive simulation of calorimeter showers with normalizing flows

Simulating particle detector response is the single most expensive step in the Large Hadron Collider computational pipeline. Recently it was shown that normalizing flows can accelerate this process while achieving unprecedented levels of accuracy, but scaling this approach up to higher resolutions relevant for future detector upgrades leads to prohibitive memory constraints. To overcome this problem, we introduce Inductive CaloFlow (icaloflow), a framework for fast detector simulation based on an inductive series of normalizing flows trained on the pattern of energy depositions in pairs of consecutive calorimeter layers. We further use a teacher-student distillation to increase sampling speed without loss of expressivity. As we demonstrate with datasets 2 and 3 of the CaloChallenge2022, icaloflow can realize the potential of normalizing flows in performing fast, high-fidelity simulation on detector geometries that are $\sim 10–100$ times higher granularity than previously considered.