AUTHOR=Jawahar Pratik , Aarrestad Thea , Chernyavskaya Nadezda , Pierini Maurizio , Wozniak Kinga A. , Ngadiuba Jennifer , Duarte Javier , Tsan Steven 

TITLE=Improving Variational Autoencoders for New Physics Detection at the LHC With Normalizing Flows

JOURNAL=Frontiers in Big Data

VOLUME=5

YEAR=2022

URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2022.803685

DOI=10.3389/fdata.2022.803685

ISSN=2624-909X

ABSTRACT=<p>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.</p>