Speaker
Marco Bellagente
(Universität Heidelberg)
Description
ML tools based on generative models, such as cycleGANs and invertible architectures, can be used to address the problem of unfolding detector effects, a challenge for data analysis at hadronic colliders.
Authors
Marco Bellagente
(Universität Heidelberg)
Anja Butter
Tilman Plehn
Ramon Winterhalder
(Universität Heidelberg)
