Subtracting event samples is a common task in LHC simulation and analysis, and standard solutions tend to be inefficient. We employ generative adversarial networks to produce new event samples with a phase space distribution corresponding to added or subtracted input samples. We first illustrate for a toy example how such a network beats the statistical limitations of the training data. We then show how such a network can be used to subtract background events or to include non-local collinear subtraction events at the level of unweighted 4-vector events.
@article{arxiv.1912.08824,
title = {How to GAN Event Subtraction},
author = {Anja Butter and Tilman Plehn and Ramon Winterhalder},
journal= {arXiv preprint arXiv:1912.08824},
year = {2020}
}