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Media type:
E-Article
Title:
Deep learning based impact parameter determination for the CBM experiment
Contributor:
Omana Kuttan, Manjunath
[Author];
Steinheimer, Jan
[Author];
Zhou, Kai
[Author];
Redelbach, Andreas
[Author];
Stöcker, Horst
[Author]
imprint:
Publication Server of Goethe University Frankfurt am Main, 2021-02-02
Language:
English
DOI:
https://doi.org/10.3390/particles4010006
Origination:
Footnote:
Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
Description:
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.