  We seek to remove foreground contaminants from 21cm intensity mapping
observations. We demonstrate that a deep convolutional neural network (CNN)
with a UNet architecture and three-dimensional convolutions, trained on
simulated observations, can effectively separate frequency and spatial patterns
of the cosmic neutral hydrogen (HI) signal from foregrounds in the presence of
noise. Cleaned maps recover cosmological clustering statistics within 10% at
all relevant angular scales and frequencies. This amounts to a reduction in
prediction variance of over an order of magnitude on small angular scales
($\ell > 300$), and improved accuracy for small radial scales ($k_{\parallel} >
0.17\ \rm h\ Mpc^{-1})$ compared to standard Principal Component Analysis (PCA)
methods. We estimate posterior confidence intervals for the network's
prediction by training an ensemble of UNets. Our approach demonstrates the
feasibility of analyzing 21cm intensity maps, as opposed to derived summary
statistics, for upcoming radio experiments, as long as the simulated foreground
model is sufficiently realistic. We provide the code used for this analysis on
Github https://github.com/tlmakinen/deep21 as well as a browser-based tutorial
for the experiment and UNet model via the accompanying
http://bit.ly/deep21-colab Colab notebook.
