A fast centrality-meter for heavy-ion collisions at the CBM experiment

A new method of event characterization based on Deep Learning is presented. The PointNet models can be used for fast, online event-by-event impact parameter determination at the CBM experiment. For this study, UrQMD and 
A new method of event characterization based on Deep Learning is presented. The PointNet models can be used for fast, online event-by-event impact parameter determination at the CBM experiment. For this study, UrQMD and the CBM detector simulation are used to generate Au+Au collision events at 10 AGeV which are then used to train and evaluate PointNet based architectures. The models can be trained on features like the hit position of particles in the CBM detector planes, tracks reconstructed from the hits or combinations thereof. The Deep Learning models reconstruct impact parameters from 2-14 fm with a mean error varying from -0.33 to 0.22 fm. For impact parameters in the range of 5-14 fm, a model which uses the combination of hit and track information of particles has a relative precision of 4-9% and a mean error of -0.33 to 0.13 fm. In the same range of impact parameters, a model with only track information has a relative precision of 4-10% and a mean error of -0.18 to 0.22 fm. This new method of event-classification is shown to be more accurate and less model dependent than conventional methods and can utilize the performance boost of modern GPU processor units.
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Author:Manjunath Omana Kuttan, Jan Steinheimer, Kai Zhou, Andreas Redelbach, Horst Stöcker
URN:urn:nbn:de:hebis:30:3-566451
DOI:http://dx.doi.org/10.1016/j.physletb.2020.135872
ISSN:1873-2445
ISSN:0370-2693
Parent Title (German):Physics Letters B
Publisher:Elsevier
Place of publication:Amsterdam [u.a.]
Document Type:Article
Language:English
Date of Publication (online):2020/10/20
Date of first Publication:2020/10/20
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2020/10/29
Volume:811
Issue:135872
Pagenumber:8
HeBIS PPN:472986694
Institutes:Physik
Informatik
Frankfurt Institute for Advanced Studies (FIAS)
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung 4.0

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