An equation-of-state-meter for CBM using PointNet

  • A novel method for identifying the nature of QCD transitions in heavy-ion collision experiments is introduced. PointNet based Deep Learning (DL) models are developed to classify the equation of state (EoS) that drives the hydrodynamic evolution of the system created in Au-Au collisions at 10 AGeV. The DL models were trained and evaluated in different hypothetical experimental situations. A decreased performance is observed when more realistic experimental effects (acceptance cuts and decreased resolutions) are taken into account. It is shown that the performance can be improved by combining multiple events to make predictions. The PointNet based models trained on the reconstructed tracks of charged particles from the CBM detector simulation discriminate a crossover transition from a first order phase transition with an accuracy of up to 99.8%. The models were subjected to several tests to evaluate the dependence of its performance on the centrality of the collisions and physical parameters of fluid dynamic simulations. The models are shown to work in a broad range of centralities (b=0–7 fm). However, the performance is found to improve for central collisions (b=0–3 fm). There is a drop in the performance when the model parameters lead to reduced duration of the fluid dynamic evolution or when less fraction of the medium undergoes the transition. These effects are due to the limitations of the underlying physics and the DL models are shown to be superior in its discrimination performance in comparison to conventional mean observables.

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Metadaten
Author:Omana Kuttan ManjunathORCiD, Kai ZhouORCiD, Jan SteinheimerORCiDGND, Andreas Redelbach, Horst StöckerORCiDGND
URN:urn:nbn:de:hebis:30:3-701633
DOI:https://doi.org/10.1007/JHEP10(2021)184
ISSN:1029-8479
ISSN:1126-6708
Parent Title (English):Journal of high energy physics
Publisher:Springer
Place of publication:Berlin ; Heidelberg
Document Type:Article
Language:English
Year of first Publication:2002
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2022/10/05
HeBIS-PPN:504263226
Institutes:Physik / Physik
Informatik und Mathematik / Informatik
Wissenschaftliche Zentren und koordinierte Programme / Frankfurt Institute for Advanced Studies (FIAS)
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung 4.0