Deep Learning for Line Intensity Mapping Observations: Information Extraction from Noisy Maps. (arXiv:2010.00809v2 [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:+Shirasaki_M/0/1/0/all/0/1">Masato Shirasaki</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 (LIM) is a promising observational method to probe
large-scale fluctuations of line emission from distant galaxies. Data from
wide-field LIM observations allow us to study the large-scale structure of the
universe as well as galaxy populations and their evolution. A serious problem
with LIM is contamination by foreground/background sources and various noise
contributions. We develop conditional generative adversarial networks (cGANs)
that extract designated signals and information from noisy maps. We train the
cGANs using 30,000 mock observation maps with assuming a Gaussian noise matched
to the expected noise level of NASA’s SPHEREx mission. The trained cGANs
successfully reconstruct H{alpha} emission from galaxies at a target redshift
from observed, noisy intensity maps. Intensity peaks with heights greater than
3.5 {sigma} noise are located with 60 % precision. The one-point probability
distribution and the power spectrum are accurately recovered even in the
noise-dominated regime. However, the overall reconstruction performance depends
on the pixel size and on the survey volume assumed for the training data. It is
necessary to generate training mock data with a sufficiently large volume in
order to reconstruct the intensity power spectrum at large angular scales. Our
deep-learning approach can be readily applied to observational data with line
confusion and with noise.
Line intensity mapping (LIM) is a promising observational method to probe
large-scale fluctuations of line emission from distant galaxies. Data from
wide-field LIM observations allow us to study the large-scale structure of the
universe as well as galaxy populations and their evolution. A serious problem
with LIM is contamination by foreground/background sources and various noise
contributions. We develop conditional generative adversarial networks (cGANs)
that extract designated signals and information from noisy maps. We train the
cGANs using 30,000 mock observation maps with assuming a Gaussian noise matched
to the expected noise level of NASA’s SPHEREx mission. The trained cGANs
successfully reconstruct H{alpha} emission from galaxies at a target redshift
from observed, noisy intensity maps. Intensity peaks with heights greater than
3.5 {sigma} noise are located with 60 % precision. The one-point probability
distribution and the power spectrum are accurately recovered even in the
noise-dominated regime. However, the overall reconstruction performance depends
on the pixel size and on the survey volume assumed for the training data. It is
necessary to generate training mock data with a sufficiently large volume in
order to reconstruct the intensity power spectrum at large angular scales. Our
deep-learning approach can be readily applied to observational data with line
confusion and with noise.
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