SE3D: Building a radiative transfer emulator to fit panchromatic resolved galaxy observations with 3D models of dust and stars

We present a framework for analysing panchromatic and spatially resolved galaxy observations, dubbed SE3D. SE3D simultaneously and self-consistently models a galaxy’s spectral energy distribution and its spectral distributions of global structural parameters: the wavelength-dependent galaxy size, light profile, and projected axis ratio. To this end, it employs a machine learning emulator trained on a large library of toy model galaxies processed with 3D dust radiative transfer and mock-observed under a range of viewing angles. The toy models vary in their stellar and dust geometries, and include radial stellar population gradients. The computationally efficient machine learning emulator uses a Bayesian neural network architecture, and reproduces the spectral distributions at an accuracy of $\sim 0.05$ dex or less across the dynamic range of input parameters, and across the rest-frame UVJ colour space spanned by observed galaxies. We carry out a sensitivity analysis demonstrating that the emulator has successfully learned the intricate mappings between galaxy physical properties and direct observables (fluxes, colours, sizes, size ratios between different wavebands, etc.). We further discuss the physical conditions giving rise to a range of total-to-selective attenuation ratios, $R_V$, with among them most prominently the projected dust surface mass density.