A Normalized Autoencoder for LHC Triggers

TL;DR

This paper introduces a normalized autoencoder tailored for LHC trigger analysis, effectively identifying anomalous jets and outperforming existing autoencoders in detecting signals like dark jets.

Contribution

The paper presents the first symmetric autoencoder with a probabilistic framework for LHC anomaly detection, improving performance over previous methods.

Findings

Outperforms existing autoencoders in top vs QCD jet classification

Reliably detects various dark-jet signals

Provides a symmetric anomaly detection approach

Abstract

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.