Deep learning reconstruction of three-dimensional galaxy distributions with intensity mapping observations. (arXiv:2110.05755v3 [astro-ph.GA] UPDATED)
<a href="http://arxiv.org/find/astro-ph/1/au:+Moriwaki_K/0/1/0/all/0/1">Kana Moriwaki</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Yoshida_N/0/1/0/all/0/1">Naoki Yoshida</a>
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.
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.
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