  We explore the possibility of using deep learning to generate multifield
images from state-of-the-art hydrodynamic simulations of the CAMELS project. We
use a generative adversarial network to generate images with three different
channels that represent gas density (Mgas), neutral hydrogen density (HI), and
magnetic field amplitudes (B). The quality of each map in each example
generated by the model looks very promising. The GAN considered in this study
is able to generate maps whose mean and standard deviation of the probability
density distribution of the pixels are consistent with those of the maps from
the training data. The mean and standard deviation of the auto power spectra of
the generated maps of each field agree well with those computed from the maps
of IllustrisTNG. Moreover, the cross-correlations between fields in all
instances produced by the emulator are in good agreement with those of the
dataset. This implies that all three maps in each output of the generator
encode the same underlying cosmology and astrophysics.
