  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 ~ 10 - 100 times higher granularity than previously
considered.
