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We present a novel implementation of the artificial intelligence autoencodingalgorithm, used as an ultrafast and ultraefficient anomaly detector, built witha forest of deep decision trees on FPGA, field programmable gate arrays.Scenarios at the Large Hadron Collider at CERN are considered, for which theautoencoder is trained using known physical processes of the Standard Model.The design is then deployed in real-time trigger systems for anomaly detectionof new unknown physical processes, such as the detection of exotic Higgsdecays, on events that fail conventional threshold-based algorithms. Theinference is made within a latency value of 25 ns, the time between successivecollisions at the Large Hadron Collider, at percent-level resource usage. Ourmethod offers anomaly detection at the lowest latency values for edge AI userswith tight resource constraints.

Nanosecond anomaly detection with decision trees for high energy physics and real-time application to exotic Higgs decays