LHC analyses directly comparing data and simulated events bear the danger of using first-principle predictions only as a black-box part of event simulation. We show how simulations, for instance, of detector effects can instead be inverted using generative networks. This allows us to reconstruct parton level information from measured events. Our results illustrate how, in general, fully conditional generative networks can statistically invert Monte Carlo simulations. As a technical by-product we show how a maximum mean discrepancy loss can be staggered or cooled.
@article{arxiv.1912.00477,
title = {How to GAN away Detector Effects},
author = {Marco Bellagente and Anja Butter and Gregor Kasieczka and Tilman Plehn and Ramon Winterhalder},
journal= {arXiv preprint arXiv:1912.00477},
year = {2022}
}