Year: 2024
Type: peer-review
Abstract: 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.
Authors: Luigi Favaro, Michael Krämer, Tanmoy Modak, Tilman Plehn, Jan Rüschkamp
Institutions: Heidelberg University, RWTH Aachen University
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Funder: Deutsche Forschungsgemeinschaft
OpenAlex ID: W4407092667