TY  - JOUR
A1  - Omana Kuttan, Manjunath
A1  - Steinheimer, Jan
A1  - Zhou, Kai
A1  - Redelbach, Andreas Ralph
A1  - Stöcker, Horst
T1  - Deep learning based impact parameter determination for the CBM experiment
T2  - Particles
N2  - 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.
KW  - heavy ion collisions
KW  - deep learning
KW  - PointNet
KW  - centrality
KW  - CBM detector
KW  - impact parameter
Y1  - 2021
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/57652
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-576527
SN  - 2571-712X
VL  - 4
SP  - 47
EP  - 52
PB  - MDPI
CY  - Basel
ER  -