Nanosecond anomaly detection with decision trees and real-time application to exotic Higgs decays

Abstract

We present an interpretable implementation of the autoencoding algorithm, used as an anomaly detector, built with a forest of deep decision trees on FPGA, field programmable gate arrays. Scenarios at the Large Hadron Collider at CERN are considered, for which the autoencoder is trained using known physical processes of the Standard Model. The design is then deployed in real-time trigger systems for anomaly detection of unknown physical processes, such as the detection of rare exotic decays of the Higgs boson. The inference is made with a latency value of 30 ns at percent-level resource usage using the Xilinx Virtex UltraScale+ VU9P FPGA. Our method offers anomaly detection at low latency values for edge AI users with resource constraints.

Real-time inference of collisions using unsupervised AI for discovery is of interest in particle physics. Here, authors present the training and efficient implementation of a decision tree-based autoencoder used as an anomaly detector that executes at 30 ns on FPGA for use in edge computing.