Spatially Resolved Galaxy-Dust Modeling with Coupled Data-Driven Priors

TL;DR

This paper introduces a scalable, data-driven method for jointly modeling the spatially resolved stellar and dust properties of galaxies from multi-band images, improving the recovery of galaxy SEDs and dust maps.

Contribution

The paper presents a novel neural network-based approach that captures diverse galaxy geometries and dust distributions, scalable to large surveys like LSST, Euclid, and Roman.

Findings

Successfully applied to three SDSS galaxies.

Accurately recovers galaxy and dust properties across various geometries.

Enhances understanding of dust production and transport.

Abstract

A notorious problem in astronomy is the recovery of the true shape and spectral energy distribution (SED) of a galaxy despite attenuation by interstellar dust embedded in the same galaxy. This problem has been solved for a few hundred nearby galaxies with exquisite data coverage, but these techniques are not scalable to the billions of galaxies soon to be observed by large wide-field surveys like LSST, Euclid, and Roman. We present a method for jointly modeling the spatially resolved stellar and dust properties of galaxies from multi-band images. To capture the diverse geometries of galaxies, we consider non-parametric morphologies, stabilized by two neural networks that act as data-driven priors: the first informs our inference of the galaxy's underlying morphology, the second constrains the galaxy's dust morphology conditioned on our current estimate of the galaxy morphology. We demonstrate with realistic simulations that we can recover galaxy host and dust properties over a wide range of attenuation levels and geometries. We successfully apply our joint galaxy-dust model to three local galaxies observed by SDSS. In addition to improving estimates of unattenuated galaxy SEDs, our inferred dust maps will facilitate the study of dust production, transport, and destruction.