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 super-resolution 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.

通过使用流式超分辨率模型 SuperCalo，可以快速地将高维的细粒度量能计量器沉降（shower）从粗粒度沉降中提升，从而降低计算成本、内存需求和生成时间。此外，通过 SuperCalo 提升的沉降具有高度的变异性，使得可以从较少的粗粒度沉降中高保真地提升大量高维沉降，进一步减少了生成时间。