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

Roche, S. T.; Bayer, Q.; Carlson, B. T.; Ouligian, W. C.; Serhiayenka, P.; Stelzer, J.; Hong, T. M.

doi:10.1038/s41467-024-47704-8

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

Journal Article · Thu Apr 25 00:00:00 EDT 2024 · Nature Communications

DOI:https://doi.org/10.1038/s41467-024-47704-8· OSTI ID:2340950

; ; ; Ouligian, W. C.; Serhiayenka, P.; ;

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.

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Sponsoring Organization:: USDOE Office of Science (SC), High Energy Physics (HEP)

Grant/Contract Number:: SC0007914; SC0012704

OSTI ID:: 2340950

Alternate ID(s):: OSTI ID: 2472316

Journal Information:: Nature Communications, Journal Name: Nature Communications Journal Issue: 1 Vol. 15; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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