QCD or what?

Theo Heimel, Gregor Kasieczka, Tilman Plehn, Jennifer Thompson
2019 SciPost Physics  
Autoencoder networks, trained only on QCD jets, can be used to search for anomalies in jet-substructure. We show how, based either on images or on 4-vectors, they identify jets from decays of arbitrary heavy resonances. To control the backgrounds and the underlying systematics we can de-correlate the jet mass using an adversarial network. Such an adversarial autoencoder allows for a general and at the same time easily controllable search for new physics. Ideally, it can be trained and applied
more » ... data in the same phase space region, allowing us to efficiently search for new physics using un-supervised learning.
doi:10.21468/scipostphys.6.3.030 fatcat:m7yrqkxnozhbdjetu5r7ulmq74