Calorimeter shower superresolution
arxiv(2023)
摘要
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 upsampled 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
upsampled by SuperCalo possess a high degree of variation. This allows a large
number of high-dimensional calorimeter showers to be upsampled from much fewer
coarse showers with high-fidelity, which results in additional reduction in
generation time.
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