Year: 2025
Type: article
Abstract: Event-by-event QCD kinetic theory simulations are hindered by the large numerical cost of evaluating the high-dimensional collision integral in the Boltzmann equation. In this work, we show that a neural network can be used to obtain an accurate estimate of the collision integral in a fraction of the time required for the ordinary Monte Carlo evaluation of the integral. We demonstrate that for isotropic and anisotropic distribution functions, the network accurately predicts the time evolution of the distribution function, which we verify by performing traditional evaluations of the collision integral and comparing several moments of the distribution function. This work sets the stage for an event-by-event modeling of the pre-equilibrium initial stages in heavy-ion collisions.
Source: The European Physical Journal C
Institutions: University of Stavanger, TU Wien
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: diamond
Funders: European Commission, Österreichischen Akademie der Wissenschaften, Austrian Science Fund, Technische Universität Wien, Ministerio de Ciencia y Tecnología, Consellería de Cultura, Educación e Ordenación Universitaria, Xunta de Galicia, Xunta de Galicia, European Research Council, Agencia Estatal de Investigación
OpenAlex ID: W4416222097