Work

Machine learning approach to QCD kinetic theory

Year: 2025

Type: preprint

Abstract: The effective kinetic theory (EKT) 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 for a comprehensive phenomenological modeling. 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.

Source: arXiv (Cornell University)

Authors: Cabodevila, Sergio Barrera, Kurkela, Aleksi, Lindenbauer, Florian

Institutions: Universidade de Santiago de Compostela, University of Stavanger, TU Wien, Massachusetts Institute of Technology

Language: English

Topic: High-Energy Particle Collisions Research

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, Österreichischen Akademie der Wissenschaften, Austrian Science Fund, Technische Universität Wien, Xunta de Galicia, Agencia Estatal de Investigación

Awards: G1086978037, G2262748287, G3480869486, G4042783231, G4495373907, G4776079577, G5080475149, G592137030, G7084143925, G7567520714

OpenAlex ID: W7084446228

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