Calorimeter shower superresolution
Ian Pang, David Shih, J. A. Raine
Abstract
Calorimeter shower simulation is a major bottleneck in the Large Hadron Collider computational pipeline. There have been recent efforts to employ deep-generative surrogate models to overcome this challenge. However, many of best-performing models have training and generation times that do not scale well to high-dimensional calorimeter showers. In this work, we introduce supercalo, a flow-based superresolution model, and demonstrate that high-dimensional fine-grained calorimeter showers can be quickly up-sampled from coarse-grained showers. This novel approach presents a way to reduce computational cost, memory requirements, and generation time associated with fast calorimeter simulation models. Additionally, we show that the showers up-sampled by supercalo possess a high degree of variation. This allows a large number of high-dimensional calorimeter showers to be up-sampled from much fewer coarse showers with high fidelity, which results in additional reduction in generation time.