Pion/Kaon Identification at STCF DTOF Based on Classical/Quantum Convolutional Neural Network.

Particle identification (PID) is one of the most fundamental tools in various physics research conducted in collider experiments. In recent years, machine learning methods have gradually become one of the mainstream methods in the PID field of high-energy physics experiments, often providing superior performance. The emergence of quantum machine learning may potential arm a powerful new toolbox for machine learning. In this work, targeting at the π±/K± discrimination problem at the STCF experiment, a convolutional neural network (CNN) in the endcap PID system is developed. By combining the hit position and arrival time of each Cherenkov photon at the sensors, a two-dimensional pixel map is constructed as the CNN input. The preliminary results show that the CNN model has a promising performance. In addition, based on the classical CNN, a quantum convolution neural network (QCNN) is developed as well, exploring possible quantum advantages provided by quantum machine learning methods. [ABSTRACT FROM AUTHOR]