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Physics & Astronomy

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March 6, 2018 | 2:30 p.m. - 3:45 p.m.
Category: Seminar
Location: Physics & Astronomy Department - Liberal Arts and Sciences #245 | Map
666 W. Hancock
Detroit, MI 48201
Cost: Free
Audience: Academic Staff, Alumni, Community, Current Graduate Students, Current Undergraduate Students, Faculty

(Please note the special day and time for this colloquium)

"Exploring the quantum chromodynamics phase transition with deep learning"

LongGang Pang, Lawrence Berkeley National Laboratory

Abstract: The state-of-the-art pattern recognition method in machine learning (deep convolution neural network) has been used to classify two different phase transitions between normal nuclear matter and hot-dense quark gluon plasma. Big amount of training data is prepared by simulating heavy ion collisions with the most efficient relativistic hydrodynamic program CLVisc. High level correlations of particle spectra in transverse momentum and azimuthal angle learned by the neural network are quite robust in deciphering the transition type in the quantum chromodynamics phase diagram. Through this study we demonstrated that there is a traceable encoder of the phase structure that survives the dynamical evolution and exists in the final snap shot of heavy ion collisions and one can exclusively and effectively decode these information from the highly complex output using machine learning.

For more information about this event, please contact W.J. Llope at 3135779805 or