Identifying QCD transition using deep learning
- In this proceeding we review our recent work using supervised learning with a deep convolutional neural network (CNN) to identify the QCD equation of state (EoS) employed in hydrodynamic modeling of heavy-ion collisions given only final-state particle spectra ρ(pT, Ф). We showed that there is a traceable encoder of the dynamical information from phase structure (EoS) that survives the evolution and exists in the final snapshot, which enables the trained CNN to act as an effective “EoS-meter” in detecting the nature of the QCD transition.
Author: | Kai ZhouORCiD, Long-Gang PangORCiD, Nan SuORCiDGND, Hannah PetersenORCiDGND, Horst StöckerORCiDGND, Xin-Nian WangORCiDGND |
---|---|
URN: | urn:nbn:de:hebis:30:3-335777 |
DOI: | https://doi.org/10.1051/epjconf/201817116005 |
ISSN: | 2100-014X |
Parent Title (English): | EPJ Web of Conferences |
Publisher: | EDP Sciences |
Place of publication: | Les Ulis |
Document Type: | Conference Proceeding |
Language: | English |
Year of Completion: | 2018 |
Date of first Publication: | 2018/02/02 |
Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
Contributing Corporation: | 17th International Conference on Strangeness in Quark Matter (SQM 2017) |
Release Date: | 2019/04/04 |
Volume: | 171 |
Issue: | Article Number 16005 |
Page Number: | 4 |
HeBIS-PPN: | 448535610 |
Institutes: | Physik / Physik |
Dewey Decimal Classification: | 5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik |
Sammlungen: | Universitätspublikationen |
Licence (German): | Creative Commons - Namensnennung 4.0 |