What's anomalous in LHC jets?
Thorsten Buss, Barry M. Dillon, Thorben Finke, Michael Krämer, Alessandro Morandini, Alexander Mück, Ivan Oleksiyuk, Tilman Plehn
SciPost Phys. 15, 168 (2023) · published 17 October 2023
- doi: 10.21468/SciPostPhys.15.4.168
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Abstract
Searches for anomalies are a significant motivation for the LHC and help define key analysis steps, including triggers. We discuss specific examples how LHC anomalies can be defined through probability density estimates, evaluated in a physics space or in an appropriate neural network latent space, and discuss the model-dependence in choosing an appropriate data parameterisation. 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.
Cited by 11
Authors / Affiliations: mappings to Contributors and Organizations
See all Organizations.- 1 Thorsten Buss,
- 1 Barry M. Dillon,
- 2 Thorben Finke,
- 2 Michael Krämer,
- 2 Alessandro Morandini,
- 2 Alexander Mück,
- 2 Ivan Oleksiyuk,
- 1 Tilman Plehn
- 1 Ruprecht-Karls-Universität Heidelberg / Heidelberg University
- 2 Rheinisch-Westfälische Technische Hochschule Aachen / RWTH Aachen University [RWTH]