Article ; Online: Improving Variational Autoencoders for New Physics Detection at the LHC With Normalizing Flows.
Frontiers in big data
2022 Volume 5, Page(s) 803685
Abstract: We investigate how to improve new physics detection strategies exploiting variational autoencoders and normalizing flows for anomaly detection at the Large Hadron Collider. As a working example, we consider the DarkMachines challenge dataset. We show how ...
| Abstract | We investigate how to improve new physics detection strategies exploiting variational autoencoders and normalizing flows for anomaly detection at the Large Hadron Collider. As a working example, we consider the DarkMachines challenge dataset. We show how different design choices (e.g., event representations, anomaly score definitions, network architectures) affect the result on specific benchmark new physics models. Once a baseline is established, we discuss how to improve the anomaly detection accuracy by exploiting normalizing flow layers in the latent space of the variational autoencoder. |
|---|---|
| Language | English |
| Publishing date | 2022-02-28 |
| Publishing country | Switzerland |
| Document type | Journal Article |
| ISSN | 2624-909X |
| ISSN (online) | 2624-909X |
| DOI | 10.3389/fdata.2022.803685 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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