SciPost Submission Page
What's Anomalous in LHC Jets?
by Thorsten Buss, Barry M. Dillon, Thorben Finke, Michael Krämer, Alessandro Morandini, Alexander Mück, Ivan Oleksiyuk, Tilman Plehn
This Submission thread is now published as
Submission summary
| Submission information |
| Preprint Link: |
scipost_202207_00012v2
(pdf)
|
| Date accepted: |
2023-09-20 |
| Date submitted: |
2023-08-14 16:23 |
| Submitted by: |
Finke, Thorben |
| Submitted to: |
SciPost Physics |
| Ontological classification |
| Academic field: |
Physics |
| Specialties: |
- High-Energy Physics - Phenomenology
|
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.
