Research on Charged Particle Identification of Telescope in Heavy-Ion Collisions at Low and Intermediate Energies Based on Optimization Algorithms

Particle identification plays an important role in heavy-ion collisions at low and intermediate energies. The task of particle identification is to identify the particles’ atomic number (Z) and mass number (A) in heavy-ion collisions. However, traditional particle identification methods have several problems, including experience-dependency, poor repeatability, and time-consuming challenges. This paper proposes a particle identification method based on particle swarm optimization (PSO) to overcome these challenges. The verification by Geant4 simulation data show that it can effectively identify particle data of heavy-ion collisions at low and intermediate energies. Compared with traditional supervised and unsupervised learning algorithms, this method can reduce data requirements and effectively address complex data distribution. It is expected to become the technical basis for developing professional particle identification software.