Track Finding with Deep Neural Networks
DOI:
https://doi.org/10.7494/csci.2019.20.4.3376Keywords:
Deep Neural Networks, Machine Learning, tracking, HEPAbstract
High Energy Physics experiments require fast and efficient methods toreconstruct 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-dimensional model. It is
planned to apply this method to the experimental data taken by the MUonE
experiment at CERN.
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