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What's Anomalous in LHC Jets?
by Thorsten Buss, Barry M. Dillon, Thorben Finke, Michael Krämer, Alessandro Morandini, Alexander Mück, Ivan Oleksiyuk, Tilman Plehn
Submission summary
| Authors (as registered SciPost users): | Thorsten Buss · Barry M. Dillon · Thorben Finke · Michael Krämer · Tilman Plehn |
| Submission information | |
|---|---|
| Preprint Link: | scipost_202207_00012v2 (pdf) |
| Date accepted: | Sept. 20, 2023 |
| Date submitted: | Aug. 14, 2023, 4:23 p.m. |
| Submitted by: | Thorben Finke |
| Submitted to: | SciPost Physics |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
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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 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.
Current status:
Editorial decision:
For Journal SciPost Physics: Publish
(status: Editorial decision fixed and (if required) accepted by authors)