TY  - JOUR
AU  - Omana Kuttan, Manjunath
AU  - Steinheimer, Jan
AU  - Zhou, Kai
AU  - Redelbach, Andreas
AU  - Stoecker, Horst
TI  - A fast centrality-meter for heavy-ion collisions at the CBM experiment
JO  - Physics letters / B
VL  - 811
IS  - arXiv:2009.01584
SN  - 0370-2693
CY  - Amsterdam
PB  - North-Holland Publ.
M1  - GSI-2021-00718
M1  - arXiv:2009.01584
SP  - 135872
PY  - 2020
N1  - This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Funded by SCOAP3.
AB  - 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 A GeV 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
KW  - heavy ion: scattering (INSPIRE)
KW  - quantum molecular dynamics: relativistic (INSPIRE)
KW  - CBM (INSPIRE)
KW  - impact parameter (INSPIRE)
KW  - neural network (INSPIRE)
KW  - tracks (INSPIRE)
KW  - performance (INSPIRE)
KW  - numerical methods (INSPIRE)
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000612225400024
DO  - DOI:10.1016/j.physletb.2020.135872
UR  - https://repository.gsi.de/record/238173
ER  -