Deconfusing intensity maps with neural networks

Understand

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.