Speaker
Luigi Favaro
(ITP - University of Heidelberg)
Description
We present DarkCLR, a novel framework for detecting semi-visible jets at the LHC. DarkCLR uses a self-supervised contrastive-learning approach to create observables that are approximately invariant under relevant transformations. We use background-enhanced data to create a sensitive representation and evaluate the representations using a normalized autoencoder as a density estimator. Our results show a remarkable sensitivity for a wide range of semi-visible jets and are more robust than a supervised classifier trained on a specific signal.
Primary authors
Jan Rüschkamp
Luigi Favaro
(ITP - University of Heidelberg)
Michael Krämer
(RWTH Aachen)
Tanmoy Modak
(Heidelberg University)
Tilman Plehn
