Development of ML FPGA Filter for Particle Identification and Tracking in Real Time - Barbosa, F.; Belfore, L.; Branson, N.; Dickover, C.; Fanelli, C.; Furletov, D.; Furletov, S.; Jokhovets, L.; Lawrence, D.; Romanov, D. (IEEE transactions on nuclear science, 2023)
Real-time data processing is a frontier field in experimental particle physics. Machine learning (ML) methods are widely used and have proven to be very powerful in particle physics. The growing computational power of modern field programmable gate array (FPGA) boards allows us to add more sophisticated algorithms for real-time data processing. Many tasks could be solved using modern ML algorithms which are naturally suited for FPGA architectures. The FPGA-based ML algorithm provides an extremely low, sub-microsecond, latency decision and makes information-rich datasets for event selection. Work has started to evaluate an FPGA-based ML algorithm for a real-time particle identification and tracking with transition radiation detector (TRD) and e/m calorimeter. The first target is the GlueX experiment, with a plan to build a TRD based on GEM technology (GEMTRD). GlueX trigger latency is 3.3~μ \text{s} .