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We investigate how to improve new physics detection strategies exploitingvariational autoencoders and normalizing flows for anomaly detection at theLarge Hadron Collider. As a working example, we consider the DarkMachineschallenge dataset. We show how different design choices (e.g., eventrepresentations, anomaly score definitions, network architectures) affect theresult on specific benchmark new physics models. Once a baseline isestablished, we discuss how to improve the anomaly detection accuracy byexploiting normalizing flow layers in the latent space of the variationalautoencoder.

Improving Variational Autoencoders for New Physics Detection at the LHC with Normalizing Flows