A Normalized Autoencoder for LHC Triggers

Understand

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.