A forward-modelling approach to overcome point spread function smearing and fit flexible models to the chemical structure of galaxies

Historically, metallicity profiles of galaxies have been modelled using a radially symmetric, two-parameter linear model, which reveals that most galaxies are more metal-rich in their central regions than their outskirts. However, this model is known to yield inaccurate results when the point spread function (PSF) of a telescope is large. Furthermore, a radially symmetric model cannot capture asymmetric structures within a galaxy. In this work, we present an extension of the popular forward-modelling Python package lenstronomy, which allows the user to overcome both of these obstacles. We demonstrate the new features of this code base through two illustrative examples on simulated data. First, we show that through forward modelling, lenstronomy is able to recover accurately the metallicity gradients of galaxies, even when the PSF is comparable with the size of a galaxy, as long as the data are observed with a sufficient number of pixels. Additionally, we demonstrate how lenstronomy is able to fit irregular metallicity profiles to galaxies that are not well-described by a simple surface brightness profile. This opens up pathways for detailed investigations into the connections between morphology and chemical structure for galaxies at cosmological distances using the transformative capabilities of JWST. Our code is publicly available and open source, and can also be used to model spatial distributions of other galaxy properties that are traced by its surface brightness profile.