6–12 Apr 2025
Goethe University Frankfurt, Campus Westend, Theodor-W.-Adorno-Platz 1, 60629 Frankfurt am Main, Germany
Europe/Berlin timezone

Machine learning approach to QCD kinetic theory

Not scheduled
20m
Goethe University Frankfurt, Campus Westend, Theodor-W.-Adorno-Platz 1, 60629 Frankfurt am Main, Germany

Goethe University Frankfurt, Campus Westend, Theodor-W.-Adorno-Platz 1, 60629 Frankfurt am Main, Germany

Oral New theoretical developments

Speaker

Florian Lindenbauer (TU Wien)

Description

The effective kinetic theory of QCD provides a possible picture of various non-equilibrium processes in heavy- and light-ion collisions. While there have been substantial advances in simulating the EKT in simple systems with enhanced symmetry, eventually, event-by-event simulations will be required to test this physical picture. As of now, these simulations are prohibitively expensive due to the numerical complexity of the Monte Carlo evaluation of the collision kernels. In this talk, we show how the evaluation of the collision kernels can be performed using neural networks paving the way to full event-by-event simulations.

Category Theory

Primary authors

Sergio Barrera Cabodevila (Instituto Galego de Física de Altas Enerxías - Universidade de Santiago de Compostela) Eero Aleksi Kurkela Florian Lindenbauer (TU Wien)

Presentation materials

There are no materials yet.