Deep learning reconstruction of three-dimensional galaxy distributions with intensity mapping observations

Line intensity mapping is emerging as a novel method that can measure the collective intensity fluctuations of atomic/molecular line emission from distant galaxies. Several observational programs with various wavelengths are ongoing and planned, but there remains a critical problem of line confusion; emission lines originating from galaxies at different distances are confused at an observed wavelength. We devise a generative adversarial network that extracts designated emission line signals from noisy three-dimensional data. Our novel network architecture allows two input data at different wavelengths so that it discerns the co-existence and the correlation of two targeted lines, $\rm H\alpha$ and [OIII]. After being trained with a large number of realistic mock catalogs, the network is able to reconstruct the three-dimensional distribution of emission-line galaxies at $z = 1.3-2.4$. Bright galaxies are identified with a precision of 82 percent, and the cross-correlation coefficients between the true and reconstructed intensity maps are as high as 0.8. Our deep-learning method can be readily applied to data from planned space-borne and ground-based experiments.