Artificial Neural Network For Identification Of Short-Lived Particles In The Cbm Experiment

In high energy particle colliders, detectors record millions of points of data during collision events. Therefore, good data analysis depends on distinguishing collisions which produce particles of interest (signal) from those producing other particles (background). Machine learning algorithms in the current times have become popular and useful as the method of choice for such large scale data analysis. In this work, we propose and implement an artificial neural network architecture to achieve the task of identifying precisely the parent particles from all the candidates arising out of track reconstruction from collision data in the future Compressed Baryonic Matter (CBM) experiment. Our framework performs comparably to the existing computational algorithm for this task even with a simple network architecture.