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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.
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Ian Pang
David Shih
J. A. Raine
Physical review. D/Physical review. D.
Rutgers, The State University of New Jersey
University of Geneva
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Pang et al. (Tue,) studied this question.
www.synapsesocial.com/papers/68e6a284b6db643587625e60 — DOI: https://doi.org/10.1103/physrevd.109.092009