MillimeterDL: Deep Learning Simulations of the Microwave Sky
  We present 500 high-resolution, full-sky millimeter-wave Deep Learning (DL)
simulations that include lensed CMB maps and correlated foreground components.
We find that these MillimeterDL simulations can reproduce a wide range of
non-Gaussian summary statistics matching the input training simulations, while
only being optimized to match the power spectra. The procedure we develop in
this work enables the capability to mass produce independent full-sky
realizations from a single expensive full-sky simulation, when ordinarily the
latter would not provide enough training data. We also circumvent a common
limitation of high-resolution DL simulations that they be confined to small sky
areas, often due to memory or GPU issues; we do this by developing a
"stitching" procedure that can faithfully recover the high-order statistics of
a full-sky map without discontinuities or repeated features. In addition, since
our network takes as input a full-sky lensing convergence map, it can in
principle take a full-sky lensing convergence map from any large-scale
structure (LSS) simulation and generate the corresponding lensed CMB and
correlated foreground components at millimeter wavelengths; this is especially
useful in the current era of combining results from both CMB and LSS surveys,
which require a common set of simulations.
