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

TL;DR

This paper introduces a deep learning GAN approach to reconstruct three-dimensional galaxy distributions from intensity mapping data, effectively addressing line confusion and enabling accurate galaxy identification at high redshifts.

Contribution

The study develops a novel GAN architecture that decomposes overlapping emission lines in 3D intensity maps, improving galaxy distribution reconstruction from noisy observational data.

Findings

Achieves 84% precision in identifying bright galaxies.

Cross-correlation coefficients reach up to 0.8 between true and reconstructed maps.

Successfully reconstructs galaxy distributions at redshifts 1.3-2.4.

Abstract

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 redshifts are confused at the same 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, in which the same underlying large-scale structure is traced by two emission lines of H$\rm \alpha$ and [OIII], so that the network learns the relative contributions at each wavelength and is trained to decompose the respective signals. 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 84%, 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.