Metallicity profiles of Ultra Diffuse Galaxies in NIHAO simulations

TL;DR

This study investigates the origins of stellar metallicity gradients in Ultra Diffuse Galaxies using NIHAO simulations, revealing a variety of profiles influenced by feedback and galaxy dynamics, consistent with some observations.

Contribution

It provides the first detailed comparison of simulated and observed metallicity gradients in UDGs, linking gradients to galaxy support mechanisms and star formation regions.

Findings

Simulated UDGs show flat to negative metallicity gradients, similar to Local Group dwarfs.

DF44 UDG exhibits a flat to positive metallicity gradient, differing from simulations.

Galaxy dynamics influence metallicity profiles: rotation support correlates with flat gradients, dispersion support with negative gradients.

Abstract

Supernovae feedback driven expansion has proven to be a viable mechanism to explain the average properties of Ultra Diffuse Galaxies (UDGs) such as the sizes, colors, mass and internal kinematics. Here, we explore the origin of stellar metallicity gradients in feedback driven simulated UDGs from the NIHAO project and compare them with the observed distribution of metallicity gradients of both Local Group dwarfs as well as of the recently observed UDG DF44. Simulated UDGs display a large variety of metallicity profiles, showing flat to negative gradients, similarly to what is observed in LG dwarfs, while DF44 data suggest a flat to positive gradient. The variety of metallicity gradients in simulations is set by the interplay between the radius at which star formation occurs and the subsequent supernovae feedback driven stellar redistribution: rotation supported systems tend to have flat metallicity profiles while dispersion supported galaxies show negative and steep profiles. Our results suggest that UDGs are not peculiar in what regards their metallicity gradients, when compared to regular dwarfs. Desirably, a larger observational sample of UDGs' gradients shall be available in the future, in order to test our predictions.