Abstract
Abstract
A fast artificial neural network is developed for the prediction of cosmic ray transport
in turbulent astrophysical magnetic fields. The setup is trained and tested on bespoke datasets
that are constructed with the aid of test-particle numerical simulations of relativistic cosmic
ray dynamics in synthetic stochastic fields. The neural network uses, as input, particle and field
properties and estimates transport coefficients 107 faster than standard numerical simulations
with an overall error of ∼5%.
Subject
Astronomy and Astrophysics