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

We present a novel implementation of the artificial intelligence autoencoding algorithm, used as an ultrafast and ultraefficient 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 new unknown physical processes, such as the detection of exotic Higgs decays, on events that fail conventional threshold-based algorithms. The inference is made within a latency value of 25 ns, the time between successive collisions at the Large Hadron Collider, at percent-level resource usage. Our method offers anomaly detection at the lowest latency values for edge AI users with tight resource constraints.Comment: 26 pages, 9 figures, 1 tabl

Xilinx inputs for nanosecond anomaly detection with decision trees

Files include the Xilinx IP core for xvcu9p and a generic testbench with test vectors for the 3-variable autoencoder

Xilinx inputs for nanosecond anomaly detection with decision trees for two photons and two jets

Files include the Xilinx IP core for xvcu9p and a generic testbench with test vectors for the 8-variable autoencoder for the dataset with two photons and two jets. The design corresponds to the dataset on Mendeley at http://doi.org/10.17632/44t976dyrj.