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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]
Copyright of International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 147403122
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  Data: Artificial neural network for identification of short-lived particles in the CBM experiment.
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  Data: 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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  Data: <i>Copyright of International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1142/S0217751X20430034
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        Text: English
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      – SubjectFull: Machine learning
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      – SubjectFull: Particles
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      – TitleFull: Artificial neural network for identification of short-lived particles in the CBM experiment.
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              Text: 11/30/2020
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