Report on 2312.03067v3

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.

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