Semi-visible jets, energy-based models, and self-supervision
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 a...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
SciPost
2025-02-01
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| Series: | SciPost Physics |
| Online Access: | https://scipost.org/SciPostPhys.18.2.042 |
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| Summary: | 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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| ISSN: | 2542-4653 |