Year: 2022
Type: peer-review
Abstract: Searches for anomalies are a significant motivation for the LHC and help 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, 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.
Institutions: Heidelberg University, RWTH Aachen University
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Funder: Deutsche Forschungsgemeinschaft
OpenAlex ID: W4387758782