What's anomalous in LHC jets?
Searches for anomalies are the main motivation for the LHC and define key analysis steps, including triggers. We discuss how LHC anomalies can be defined through probability density estimates, evaluated in a physics space or in an appropriate neural network latent space. We illustrate this for class...
Gespeichert in:
| Hauptverfasser: | , , , , , , , |
|---|---|
| Dokumenttyp: | Article (Journal) Kapitel/Artikel |
| Sprache: | Englisch |
| Veröffentlicht: |
12 Oct 2023
|
| Ausgabe: | Version v3 |
| In: |
Arxiv
Year: 2023, Pages: 1-30 |
| DOI: | 10.48550/arXiv.2202.00686 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.48550/arXiv.2202.00686 Verlag, kostenfrei, Volltext: http://arxiv.org/abs/2202.00686 
|
| Verfasserangaben: | Thorsten Buss, Barry M. Dillon, Thorben Finke, Michael Krämer, Alessandro Morandini, Alexander Mück, Ivan Oleksiyuk, and Tilman Plehn |
| Zusammenfassung: | Searches for anomalies are the main motivation for the LHC and define key analysis steps, including triggers. We discuss how LHC anomalies can be defined through probability density estimates, evaluated in a physics space or in an appropriate neural network latent space. We illustrate this for classical k-means clustering, a Dirichlet variational autoencoder, and invertible neural networks. For two especially challenging scenarios of jets from a dark sector we evaluate the strengths and limitations of each method. |
|---|---|
| Beschreibung: | Online veröffentlicht am 1. Februar 2022, Version 2 am 11. Februar 2022, Version 3 am 12. Oktober 2023 Gesehen am 12.10.2022 |
| Beschreibung: | Online Resource |
| DOI: | 10.48550/arXiv.2202.00686 |