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arXiv · 2312.03067

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

Luigi Favaro·Michael Krämer·Tanmoy Modak·Tilman Plehn·Jan Rüschkamp
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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 CLR-inspired anomaly score and a normalized autoencoder as density estimators. 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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Luigi Favaro, Michael Krämer, Tanmoy Modak, Tilman Plehn, Jan Rüschkamp. 2023-12-05. Semi-visible jets, energy-based models, and self-supervision. https://doi.org/10.21468/scipostphys.18.2.042

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