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Artificial neural network for identification of short-lived particles in the CBM experiment.

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Title: Artificial neural network for identification of short-lived particles in the CBM experiment.
Authors: Banerjee, Arundhati1 (AUTHOR) barundhati18@gmail.com, Kisel, Ivan2,3,4,5 (AUTHOR) I.Kisel@compeng.uni-frankfurt.de, Zyzak, Maksym5 (AUTHOR) M.Zyzak@gsi.de
Source: International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics. 11/30/2020, Vol. 35 Issue 33, pN.PAG-N.PAG. 8p.
Subjects: Artificial neural networks, Machine learning, Particles, Heavy ion collisions
Abstract: In high energy particle colliders, detectors record millions of points of data during collision events. Therefore, good data analysis depends on distinguishing collisions which produce particles of interest (signal) from those producing other particles (background). Machine learning algorithms in the current times have become popular and useful as the method of choice for such large scale data analysis. In this work, we propose and implement an artificial neural network architecture to achieve the task of identifying precisely the parent particles from all the candidates arising out of track reconstruction from collision data in the future Compressed Baryonic Matter (CBM) experiment. Our framework performs comparably to the existing computational algorithm for this task even with a simple network architecture. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:In high energy particle colliders, detectors record millions of points of data during collision events. Therefore, good data analysis depends on distinguishing collisions which produce particles of interest (signal) from those producing other particles (background). Machine learning algorithms in the current times have become popular and useful as the method of choice for such large scale data analysis. In this work, we propose and implement an artificial neural network architecture to achieve the task of identifying precisely the parent particles from all the candidates arising out of track reconstruction from collision data in the future Compressed Baryonic Matter (CBM) experiment. Our framework performs comparably to the existing computational algorithm for this task even with a simple network architecture. [ABSTRACT FROM AUTHOR]
ISSN:0217751X
DOI:10.1142/S0217751X20430034