High Energy Physics experiments require fast and efficient methods to reconstruct the tracks of charged particles. Commonly used algorithms are sequential and the CPU required increases rapidly with a number of tracks. Neural networks can speed up the process due to their capability to model complex non-linear data dependencies and finding all tracks in parallel.
In this paper we describe the application of the Deep Neural Network to the reconstruction of straight tracks in a toy two and three-dimensional models. It is planned to apply this tracking method to the experimental data taken by the MUonE experiment at CERN.
Multivariate techniques using machine learning algorithms have become an integral part in many High Energy Physics (HEP) data analyses. The article shows the gain in physics reach of the physics experiments due to the adaptation of machine learning techniques. Rapid development in the field of machine learning in the last years is a challenge for the HEP community. The open competition for machine learning experts “Higgs Boson Machine Learning Challenge” shows, that the modern techniques developed outside HEP can significantly improve the analysis of data from HEP experiments and improve the sensitivity of searches for new particles and processes.
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