Convolutional Neural Networks with Event Images for Pileup Mitigation with the ATLAS Detector

The addition of multiple, nearly simultaneous $pp$ collisions to hard-scatter collisions (pileup) is a significant challenge for most physics analyses at the LHC. Many techniques have been proposed to mitigate the impact of pileup on jets and other reconstructed objects. This study investigates the application of convolutional neural networks to pileup mitigation by treating events as images. By using as much of the available information about the event properties as possible, the neural networks are able to provide a local pileup energy correction. The impact of this correction is studied in the context of a global event observable: the missing transverse momentum ($\vec{E}_\text{T}^\text{miss}$). The $\vec{E}_\text{T}^\text{miss}$ is particularly sensitive to pileup and the potential benefits of a neural-network approach is analyzed alongside other constituent pileup mitigation techniques and the ATLAS default $\vec{E}_\text{T}^\text{miss}$ reconstruction algorithm.