  Autoencoders are an effective analysis tool for the LHC, as they represent
one of its main goal of finding physics beyond the Standard Model. The key
challenge is that out-of-distribution anomaly searches based on the
compressibility of features do not apply to the LHC, while existing
density-based searches lack performance. We present the first autoencoder which
identifies anomalous jets symmetrically in the directions of higher and lower
complexity. The normalized autoencoder combines a standard bottleneck
architecture with a well-defined probabilistic description. It works better
than all available autoencoders for top vs QCD jets and reliably identifies
different dark-jet signals.

High Energy Physics - Phenomenology