Deep learning for intensity mapping observations: component extraction
Abstract
Line intensity mapping (LIM) is an emerging observational method to study the large-scale structure of the Universe and its evolution. LIM does not resolve individual sources but probes the fluctuations of integrated line emissions. A serious limitation with LIM is that contributions of different emission lines from sources at different redshifts are all confused at an observed wavelength. We propose a deep learning application to solve this problem. We use conditional generative adversarial networks to extract designated information from LIM. We consider a simple case with two populations of emission-line galaxies; H $\rm \alpha$ emitting galaxies at $z$ = 1.3 are confused with [O III] emitters at $z$ = 2.0 in a single observed waveband at 1.5 $\mu{\textrm m}$ . Our networks trained with 30 000 mock observation maps are able to extract the total intensity and the spatial distribution of H $\rm \alpha$ emitting galaxies at $z$ = 1.3. The intensity peaks are successfully located with 74 per cent precision. The precision increases to 91 per cent when we combine five networks. The mean intensity and the power spectrum are reconstructed with an accuracy of ∼10 per cent. The extracted galaxy distributions at a wider range of redshift can be used for studies on cosmology and on galaxy formation and evolution.
- Publication:
-
Monthly Notices of the Royal Astronomical Society
- Pub Date:
- July 2020
- DOI:
- 10.1093/mnrasl/slaa088
- arXiv:
- arXiv:2002.07991
- Bibcode:
- 2020MNRAS.496L..54M
- Keywords:
-
- galaxies: high-redshift;
- cosmology: observations;
- large-scale structure of Universe;
- Astrophysics - Astrophysics of Galaxies
- E-Print:
- 6 pages, 3 figures, accepted for publication in MNRAS Letter