Predicting CMB dust foreground using galactic 21 cm data.

Understanding angular galactic foregrounds is crucial for future CMB experiments aiming to detect $B$-mode polarization from primordial gravitational waves. Using external data in generation of foreground template maps is particularly attractive, since such maps can help with cleaning but will not remove true signal. In this exploratory paper we investigate the capability of galactic 21 cm observations to predict the dust foreground in intensity. We train a neural network to predict the dust foreground as measured by the Planck Satellite from the full velocity data-cube of galactic 21 cm emission as measured by the HI4PI survey. We demonstrate that information in the velocity structure clearly improves the predictive power over both a simple integrated emission model and a simple linear model, albeit at a rather modest level. This proof of principle on temperature data indicate that it might also be possible to improve foreground polarization templates from the same input data.