Neural Networks for the Reconstruction and Separation of High Energy Particles in a Preshower Calorimeter Juan Pavez 1(B ) , Hayk Hakobyan 2 , Carlos Valle 1 , William Brooks 2 , Sergey Kuleshov 2 , and H´ ector Allende 1 1 Departamento de Inform´atica, Universidad T´ ecnica Federico Santa Mar´ ıa, CP 110-V, Valpara´ ıso, Chile juan.pavezs@alumnos.usm.cl 2 Departamento de F´ ısica, Universidad T´ ecnica Federico Santa Mar´ ıa, CP 110-V, Valpara´ ıso, Chile Abstract. 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 recon- struction 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 learn- ing methods greatly outperform cut based techniques that are commonly used in high energy physics. Keywords: Machine learning · Neural networks Reconstruction algorithm · Computational physics 1 Introduction Event reconstruction is an important task in pattern recognition. The task is inherently statistical and consist on inferring an event given the data that it pro- duced. It has applications in fields such as high energy physics, nuclear medicine and astrophysics. In particular, in high energy physics event reconstruction is used to analyze the particles produced in particle accelerators. These accelerators collide par- ticles at high velocity, producing a huge amount of secondary particles. The observation and study of the particles produced may help to understand the structure of matter and its fundamental properties. The particles produced at c  Springer International Publishing AG, part of Springer Nature 2018 M. Mendoza and S. Velast´ ın (Eds.): CIARP 2017, LNCS 10657, pp. 491–498, 2018. https://doi.org/10.1007/978-3-319-75193-1_59