Online Machine-Learning-Based Event Selection for COMET Phase-I

Published in Phys.Sci.Forum, 2023

In many modern particle physics experiments, high-rate data handling is one of the most critical challenges due to the increase in particle intensity required to achieve higher statistics. We will tackle the challenge in the COMET experiment by developing the sub-microseconds ultra-fast machine learning (ML) algorithm implemented inside FPGAs to search for the lepton flavour violation process, a 𝜇-e conversion, using the world’s most intense muon beam. Our previous study showed that a trigger algorithm based on a gradient-boosted decision tree will realise the sufficient trigger performance within 3.2 μs with a cut-based event classification. In this paper, we further investigated neural network algorithms as event classifications. For the feasibility test, a multi-layer perceptron (MLP) model was implemented inside the FPGA, and the preliminary results are presented.