Tracking with deep neural networks

Marcin Kucharczyk; Marcin Wolter

doi:10.1117/12.2538197

6 November 2019 Tracking with deep neural networks

Marcin Kucharczyk, Marcin Wolter

Proceedings Volume 11176, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2019; 111764H (2019) https://doi.org/10.1117/12.2538197
Event: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2019, 2019, Wilga, Poland

Abstract

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.

Citation Download Citation

Marcin Kucharczyk and Marcin Wolter "Tracking with deep neural networks", Proc. SPIE 11176, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2019, 111764H (6 November 2019); https://doi.org/10.1117/12.2538197

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