Muon–Electron Pulse Shape Discrimination for Water Cherenkov Detectors Based on FPGA/SoC

The distinction of secondary particles in extensive air showers, specifically muons and electrons, is one the requirements to perform a good measurement composition primary cosmic rays. We describe two methods for pulse shape detection discrimination electrons implemented on FPGA. One uses an artificial neural network (ANN) algorithm; other exploits correlation approach based finite impulse response (FIR) filters. novel hls4ml package used build ANN inference model. Both were tested Xilinx FPGA System Chip (SoC) devices: ZU9EG Zynq UltraScale+ ZC7Z020 Zynq. data set analysis was captured with acquisition system experimental site water Cherenkov detector. A comparison accuracy detection, resources utilization power consumption both presented. results show overall particle 96.62% 92.50% FIR-based correlation, execution times 848 ns 752 ns, respectively.