Neural networks for the reconstruction and separation of high energy particles in a preshower calorimeter

© Springer International Publishing AG, part of Springer Nature 2018. Particle detectors have important applications in fields such as high energy physics and nuclear medicine. For instance, they are used in huge particles accelerators to study the elementary constituents of matter. The analysis of the data produced by these detectors requires powerful statistical and computational methods, and machine learning has become a key tool for that. We propose a reconstruction algorithm for a preshower detector. The reconstruction algorithm is in charge of identifying and classifying the particles spotted by the detector. More importantly, we propose to use a machine learning algorithm to solve the problem of particle identification in difficult cases for which the reconstruction algorithm fails. We show that our reconstruction algorithm together with the machine learning rejection method are able to identify most of the incident particles. Moreover, we found that machine learning methods greatly outperform cut based techniques that are commonly used in high energy physics.