Speaker
Sergio Barrera Cabodevila
(Heidelberg University)
Description
The Standard Model of Heavy-Ion admits a kinetic theory phase as the step before thermalization. In this context, the Effective Kinetic Theory (EKT) has been widely used to describe the formation of the Quark-Gluon Plasma in systems with enhanced symmetry, leading to phenomenologically sensible results. However, the numerical complexity of the Monte Carlo evaluations of the collision kernels prohibit to explore more general geometries in event-by-event simulations. In this talk, we propose an alternative approach to calculate the EKT collision kernels by approximating them with neural networks. We find that this new technique reduces the required computational time by three orders of magnitude.
Authors
Eero Aleksi Kurkela
(University of Stavanger)
Florian Lindenbauer
(MIT Center for Theoretical Physics – a Leinweber Institute)
Sergio Barrera Cabodevila
(Heidelberg University)
Co-authors
Aleksas Mazeliauskas
Fabian Zhou
(ITP Heidelberg)
Seyed Farid Taghavi
(Technische Universitaet Muenchen (DE))