Deconfusing intensity maps with neural networks
  Line intensity maps (LIMs) are in principle sensitive to a large amount of
information about faint, distant galaxies which are invisible to conventional
surveys. However, actually extracting that information from a confused,
foreground-contaminated map can be challenging. In this work we present the
first application of convolutional neural network (CNN) to directly determine
the underlying luminosity function of a LIM, including a treatment of
extragalactic foregrounds and instrumental noise. We apply the CNN to
simulations of mock Carbon Monoxide (CO) line intensity maps similar to those
which will be produced by the currently-active COMAP experiment. We evaluate
the trained CNN on a number of noise scenarios in order to determine how robust
the network predictions are for application to realistic data. We find that, in
the ideal case where the mock data capture all of the features of the real
data, the CNN performs comparably to or better than conventional analyses.
However, the network's accuracy degrades considerably when tested on signals
and systematics outside of those it was trained on. For both intensity mapping
and cosmology as a whole, this motivates a broad-based study of whether
simulated data can ever be generated with sufficient detail to realize the
enormous potential of machine learning methods.
