SciPost Submission Page
Semi-visible jets, energy-based models, and self-supervision
by Luigi Favaro, Michael Krämer, Tanmoy Modak, Tilman Plehn, and Jan Rüschkamp
Submission summary
| Authors (as registered SciPost users): | Luigi Favaro · Tilman Plehn |
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| Preprint Link: | scipost_202312_00024v1 (pdf) |
| Code repository: | https://github.com/luigifvr/dark-clr |
| Date submitted: | 2023-12-14 15:42 |
| Submitted by: | Favaro, Luigi |
| Submitted to: | SciPost Physics |
| Ontological classification | |
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| Academic field: | Physics |
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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 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.
Current status:
In refereeing