  Cosmological galaxy formation simulations are powerful tools to understand
the complex processes that govern the formation and evolution of galaxies.
However, evaluating the realism of these simulations remains a challenge. The
two common approaches for evaluating galaxy simulations is either through
scaling relations based on a few key physical galaxy properties, or through a
set of pre-defined morphological parameters based on galaxy images. This paper
proposes a novel image-based method for evaluating the quality of galaxy
simulations using unsupervised deep learning anomaly detection techniques. By
comparing full galaxy images, our approach can identify and quantify
discrepancies between simulated and observed galaxies. As a demonstration, we
apply this method to SDSS imaging and NIHAO simulations with different physics
models, parameters, and resolution. We further compare the metric of our method
to scaling relations as well as morphological parameters. We show that anomaly
detection is able to capture similarities and differences between real and
simulated objects that scaling relations and morphological parameters are
unable to cover, thus indeed providing a new point of view to validate and
calibrate cosmological simulations against observed data.
