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.
@article{arxiv.2202.00686,
title = {What's Anomalous in LHC Jets?},
author = {Thorsten Buss and Barry M. Dillon and Thorben Finke and Michael Krämer and Alessandro Morandini and Alexander Mück and Ivan Oleksiyuk and Tilman Plehn},
journal= {arXiv preprint arXiv:2202.00686},
year = {2023}
}