Semi-visible jets, energy-based models, and self-supervision

Author:

Luigi Favaro, Michael Krämer, Tanmoy Modak, Tilman Plehn, Jan Rüschkamp

Keyword:

High Energy Physics - Phenomenology, High Energy Physics - Phenomenology (hep-ph)

journal:

date:

2023-12-05 00:00:00

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.

PDF: Semi-visible jets, energy-based models, and self-supervision.pdf