Neural Networks for the Reconstruction and Separation of High Energy Particles in a Preshower Calorimeter

  • Juan Pavez
  • Hayk Hakobyan
  • Carlos Valle
  • William Brooks
  • Sergey Kuleshov
  • Héctor Allende
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)


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.


Machine learning Neural networks Reconstruction algorithm Computational physics 



This work was partially supported by the Research Project Basal FB 0821 and by Fondecyt Project 1170123.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Juan Pavez
    • 1
  • Hayk Hakobyan
    • 2
  • Carlos Valle
    • 1
  • William Brooks
    • 2
  • Sergey Kuleshov
    • 2
  • Héctor Allende
    • 1
  1. 1.Departamento de InformáticaUniversidad Técnica Federico Santa MaríaValparaísoChile
  2. 2.Departamento de FísicaUniversidad Técnica Federico Santa MaríaValparaísoChile

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