Symmetries, safety, and self-supervision
Collider searches face the challenge of defining a representation of high-dimensional data such that physical symmetries are manifest, the discriminating features are retained, and the choice of representation is new-physics agnostic. We introduce JetCLR to solve the mapping from low-level data to o...
Gespeichert in:
| Hauptverfasser: | , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
9 June 2022
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| In: |
SciPost physics
Year: 2022, Jahrgang: 12, Heft: 6, Pages: 1-19 |
| ISSN: | 2542-4653 |
| DOI: | 10.21468/SciPostPhys.12.6.188 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.21468/SciPostPhys.12.6.188 Verlag, lizenzpflichtig, Volltext: https://scipost.org/10.21468/SciPostPhys.12.6.188 
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| Verfasserangaben: | Barry Dillon, Gregor Kasieczka, Hans Olischlager, Tilman Plehn, Peter Sorrenson and Lorenz Vogel |
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| 520 | |a Collider searches face the challenge of defining a representation of high-dimensional data such that physical symmetries are manifest, the discriminating features are retained, and the choice of representation is new-physics agnostic. We introduce JetCLR to solve the mapping from low-level data to optimized observables though self-supervised contrastive learning. As an example, we construct a data representation for top and QCD jets using a permutation-invariant transformer-encoder network and visualize its symmetry properties. We compare the JetCLR representation with alternative representations using linear classifier tests and find it to work quite well. | ||
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