  The high accuracy of detector simulation is crucial for modern particle
physics experiments. However, this accuracy comes with a high computational
cost, which will be exacerbated by the large datasets and complex detector
upgrades associated with next-generation facilities such as the High Luminosity
LHC. We explore the viability of regression-based machine learning (ML)
approaches using convolutional neural networks (CNNs) to "denoise" faster,
lower-quality detector simulations, augmenting them to produce a higher-quality
final result with a reduced computational burden. The denoising CNN works in
concert with classical detector simulation software rather than replacing it
entirely, increasing its reliability compared to other ML approaches to
simulation. We obtain promising results from a prototype based on photon
showers in the CMS electromagnetic calorimeter. Future directions are also
discussed.
