Emulating Sunyaev-Zeldovich Images of Galaxy Clusters using
  Auto-Encoders
  We develop a machine learning algorithm that generates high-resolution
thermal Sunyaev-Zeldovich (SZ) maps of novel galaxy clusters given only halo
mass and mass accretion rate. The algorithm uses a conditional variational
autoencoder (CVAE) in the form of a convolutional neural network and is trained
with SZ maps generated from the IllustrisTNG simulation. Our method can
reproduce many of the details of galaxy clusters that analytical models usually
lack, such as internal structure and aspherical distribution of gas created by
mergers, while achieving the same computational feasibility, allowing us to
generate mock SZ maps for over $10^5$ clusters in 30 seconds on a laptop. We
show that the model is capable of generating novel clusters (i.e. not found in
the training set) and that the model accurately reproduces the effects of mass
and mass accretion rate on the SZ images, such as scatter, asymmetry, and
concentration, in addition to modeling merging sub-clusters. This work
demonstrates the viability of machine-learning--based methods for producing the
number of realistic, high-resolution maps of galaxy clusters necessary to
achieve statistical constraints from future SZ surveys.
