Maqola

Track Finding with Deep Neural Networks

M. KucharczykInstitute of Nuclear Physics PAN, KrakówM. W. WolterInstitute of Nuclear Physics PAN, Kraków

Computer Sciencejournal2019en

ABI

Annotatsiya

High Energy Physics experiments require fast and efficient methods toreconstruct the tracks of charged particles. Commonly used algorithms aresequential and the CPU required increases rapidly with a number of tracks.Neural networks can speed up the process due to their capability to modelcomplex non-linear data dependencies and finding all tracks in parallel.In this paper we describe the application of the Deep Neural Networkto the reconstruction of straight tracks in a toy two-dimensional model. It isplanned to apply this method to the experimental data taken by the MUonEexperiment at CERN.

Mavzular

Advanced Neural Network Applications Domain Adaptation and Few-Shot Learning Time Series Analysis and Forecasting

Identifikatorlar

DOI: 10.7494/csci.2019.20.4.3376

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