Learning to Remove Pileup at the LHC with Jet Images

We present the Pileup Mitgation with Machine Learning (PUMML) algorithm for pileup removal at the Large Hadron Collider (LHC) based on the jet images framework using state-of-the-art machine learning techniques. We demonstrate that our algorithm outperforms existing methods on a wide range of jet observables up to pileup levels of 140 collisions per bunch crossing. We also investigate what aspects of the event our algorithms are utilizing by understanding the learned parameters of a simplified version of the model.