Deep learning reconstruction of three-dimensional galaxy distributions with intensity mapping observations. Moriwaki, K. & Yoshida, N. arXiv:2110.05755 [astro-ph], October, 2021. arXiv: 2110.05755![Deep learning reconstruction of three-dimensional galaxy distributions with intensity mapping observations [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex 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, \${\textbackslash}rm H{\textbackslash}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.
@article{moriwaki_deep_2021,
title = {Deep learning reconstruction of three-dimensional galaxy distributions with intensity mapping observations},
url = {http://arxiv.org/abs/2110.05755},
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 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, \${\textbackslash}rm H{\textbackslash}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.},
urldate = {2021-10-25},
journal = {arXiv:2110.05755 [astro-ph]},
author = {Moriwaki, Kana and Yoshida, Naoki},
month = oct,
year = {2021},
note = {arXiv: 2110.05755},
keywords = {Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},
}
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