The study of intelligent algorithm in particle identification of heavy-ion collisions at low and intermediate energies

Traditional particle identification methods face timeconsuming, experience-dependent, and poor repeatability challenges in heavy-ion collisions at low and intermediate energies. Researchers urgently need solutions to the dilemma of traditional particle identification methods. This study explores the possibility of applying intelligent learning algorithms to the particle identification of heavy-ion collisions at low and intermediate energies. Multiple intelligent algorithms, including XgBoost and TabNet, were selected to test datasets from the neutron ion multi-detector for reaction-oriented dynamics (NIMROD-ISiS) and Geant4 simulation. Tree-based machine learning algorithms and deep learning algorithms e.g. TabNet show excellent performance and generalization ability. Adding additional data features besides energy deposition can improve the algorithm's performance when the data distribution is nonuniform. Intelligent learning algorithms can be applied to solve the particle identification problem in heavy-ion collisions at low and intermediate energies.