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

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$\alpha$ 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.

arXiv comment7 pages, 5 figures, ApJL in press

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Cited by in corpus (8)

• Machine Learning for Observational Cosmology
• [CII] line intensity mapping the epoch of reionization with the Prime-Cam on FYST
• Detecting galaxy-21-cm cross-correlation during reionization
• Foreground Removal of CO Intensity Mapping Using Deep Learning
• Bayesian Multi-line Intensity Mapping
• Stochastic Super-resolution of Cosmological Simulations with Denoising Diffusion Models
• Removal of interloper contamination to line-intensity maps using correlations with ancillary tracers of the large-scale structure
• CosmoGLINT: Cosmological Generative Model for Line Intensity Mapping with Transformer