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.
@article{arxiv.2312.03067,
title = {Semi-visible jets, energy-based models, and self-supervision},
author = {Luigi Favaro and Michael Krämer and Tanmoy Modak and Tilman Plehn and Jan Rüschkamp},
journal= {arXiv preprint arXiv:2312.03067},
year = {2025}
}