Emulating Radiative Transfer in Astrophysical Environments

TL;DR

This paper introduces a surrogate model using Fourier Neural Operators and U-Nets to efficiently approximate 3D radiative transfer in astrophysics, enabling faster simulations with high accuracy.

Contribution

It presents a novel neural network architecture that significantly accelerates radiative transfer calculations in astrophysics simulations.

Findings

Achieves over 100x speedup in radiative transfer computations.

Maintains an average relative error below 3%.

Potential for integration into hydrodynamic simulations.

Abstract

Radiative transfer is a fundamental process in astrophysics, essential for both interpreting observations and modeling thermal and dynamical feedback in simulations via ionizing radiation and photon pressure. However, numerically solving the underlying radiative transfer equation is computationally intensive due to the complex interaction of light with matter and the disparity between the speed of light and the typical gas velocities in astrophysical environments, making it particularly expensive to include the effects of on-the-fly radiation in hydrodynamic simulations. This motivates the development of surrogate models that can significantly accelerate radiative transfer calculations while preserving high accuracy. We present a surrogate model based on a Fourier Neural Operator architecture combined with U-Nets. Our model approximates three-dimensional, monochromatic radiative transfer in time-dependent regimes, in absorption-emission approximation, achieving speedups of more than 2 orders of magnitude while maintaining an average relative error below 3%, demonstrating our approach's potential to be integrated into state-of-the-art hydrodynamic simulations.