Description
Galaxy growth has traditionally been studied from two complementary perspectives: the build-up of stellar mass as inferred from galaxy-integrated SED modelling, and the growth in size as captured by high-resolution imaging. Deep multi-wavelength observations are increasingly yielding rich datasets on high-redshift galaxies from UV to mm wavelengths. A common characteristic of such datasets is that not all bands are imaged at the same resolution, with a mixture of highly resolved, marginally resolved and unresolved data. We developed a new modelling tool aimed at maximally exploiting all of this rich yet varied information without sacrificing unresolved or poorly resolved bands, and without sacrificing resolution where available. Our modelling approach is fundamentally 3D in nature, and extracts information on stellar populations and their radial variation as well as star-dust geometries, without imposing an attenuation law a priori. We jointly model observational constraints on wavelength dependent fluxes, sizes and light profile shapes using a computationally efficient machine learning emulator trained on dust radiative transfer calculations of an extensive library of toy model galaxies. Applications to observed and simulated galaxies will be discussed.
