Deep learning based impact parameter determination for the CBM experiment

  • In this talk we presented a novel technique, based on Deep Learning, to determine the impact parameter of nuclear collisions at the CBM experiment. PointNet based Deep Learning models are trained on UrQMD followed by CBMRoot simulations of Au+Au collisions at 10 AGeV to reconstruct the impact parameter of collisions from raw experimental data such as hits of the particles in the detector planes, tracks reconstructed from the hits or their combinations. The PointNet models can perform fast, accurate, event-by-event impact parameter determination in heavy ion collision experiments. They are shown to outperform a simple model which maps the track multiplicity to the impact parameter. While conventional methods for centrality classification merely provide an expected impact parameter distribution for a given centrality class, the PointNet models predict the impact parameter from 2–14 fm on an event-by-event basis with a mean error of −0.33 to 0.22 fm.

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Author:Manjunath Omana KuttanORCiD, Jan SteinheimerORCiDGND, Kai ZhouORCiD, Andreas RedelbachORCiD, Horst StöckerORCiDGND
Parent Title (English):Particles
Place of publication:Basel
Document Type:Article
Date of Publication (online):2021/02/02
Date of first Publication:2021/02/02
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2021/02/04
Tag:CBM detector; PointNet; centrality; deep learning; heavy ion collisions; impact parameter
Page Number:6
First Page:47
Last Page:52
Institutes:Physik / Physik
Informatik und Mathematik / Informatik
Wissenschaftliche Zentren und koordinierte Programme / Frankfurt Institute for Advanced Studies (FIAS)
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik
Licence (German):License LogoCreative Commons - Namensnennung 4.0