What's anomalous in LHC jets?
SciPost Physics, vol. 15
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.
Authors 8
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Affiliation as printed
Heidelberg University
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Affiliation as printed
Heidelberg University
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Affiliation as printed
RWTH Aachen University
Institute for Theoretical Particle Physics and Cosmology (TTK), RWTH Aachen University, Germany
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Affiliation as printed
RWTH Aachen University
Institute for Theoretical Particle Physics and Cosmology (TTK), RWTH Aachen University, Germany
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Affiliation as printed
RWTH Aachen University
Institute for Theoretical Particle Physics and Cosmology (TTK), RWTH Aachen University, Germany
-
Affiliation as printed
RWTH Aachen University
Institute for Theoretical Particle Physics and Cosmology (TTK), RWTH Aachen University, Germany
-
Affiliation as printed
RWTH Aachen University
Institute for Theoretical Particle Physics and Cosmology (TTK), RWTH Aachen University, Germany
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Affiliation as printed
Heidelberg University
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