  Generative models producing images have enormous potential to advance
discoveries across scientific fields and require metrics capable of quantifying
the high dimensional output. We propose that astrophysics data, such as galaxy
images, can test generative models with additional physics-motivated ground
truths in addition to human judgment. For example, galaxies in the Universe
form and change over billions of years, following physical laws and
relationships that are both easy to characterize and difficult to encode in
generative models. We build a conditional denoising diffusion probabilistic
model (DDPM) and a conditional variational autoencoder (CVAE) and test their
ability to generate realistic galaxies conditioned on their redshifts (galaxy
ages). This is one of the first studies to probe these generative models using
physically motivated metrics. We find that both models produce comparable
realistic galaxies based on human evaluation, but our physics-based metrics are
better able to discern the strengths and weaknesses of the generative models.
Overall, the DDPM model performs better than the CVAE on the majority of the
physics-based metrics. Ultimately, if we can show that generative models can
learn the physics of galaxy evolution, they have the potential to unlock new
astrophysical discoveries.
