Nanosecond machine learning regression with deep boosted decision trees in FPGA for high energy physics

We present a novel application of the machine learning / artificial intelligence method called boosted decision trees to estimate physical quantities on field programmable gate arrays (FPGA). The software package fwXmachina features a new architecture called parallel decision paths that allows for deep decision trees with arbitrary number of input variables. It also features a new optimization scheme to use different numbers of bits for each input variable, which produces optimal physics results and ultraefficient FPGA resource utilization. Problems in high energy physics of proton collisions at the Large Hadron Collider (LHC) are considered. Estimation of missing transverse momentum (ETmiss) at the first level trigger system at the High Luminosity LHC (HL-LHC) experiments, with a simplified detector modeled by Delphes, is used to benchmark and characterize the firmware performance. The firmware implementation with a maximum depth of up to 10 using eight input variables of 16-bit precision gives a latency value of O(10) ns, independent of the clock speed, and O(0.1)% of the available FPGA resources without using digital signal processors.Comment: 27 pages, 14 figures, 5 table

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