Simplified Methods of Particle Trajectory Generation in Time Projection Chamber for Machine Learning Based Particle Momentum Classification

ALICE is one of the four biggest experiment in CERN's Large Hadron Collider (LHC), focused on the heavy ion collisions. Time Projection Chamber (TPC) is one of the detectors installed in ALICE, it is the main device for pattern recognition, tracking, and identification of charged particles. Data rate is extremely high but not every data recorded are useful. Many attempts have been done to classify the useless data, one of the most popular is using Machine Learning (ML), but training sets is needed for ML to operate. In this paper, a brief explanation of multiple scattering, space charge and energy loss of the particle tracks are provided, we discuss the TPC simulation strategy, and the development of the tracks generator. This paper has led to the development of a simplified method to generate training sets for ML with the freedom to choose the initial parameter and the number of particle multiplicity.