Inpainting Galactic Foreground Intensity and Polarization maps using Convolutional Neural Network. (arXiv:2003.13691v1 [astro-ph.CO])
<a href="http://arxiv.org/find/astro-ph/1/au:+Puglisi_G/0/1/0/all/0/1">Giuseppe Puglisi</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Bai_X/0/1/0/all/0/1">Xiran Bai</a>
Deep convolutional neural networks have been a popular tool for image
generation and restoration. The performance of these networks is related to the
capability of learning realistic features from a large dataset. In this work,
we applied the problem of inpainting non-Gaussian signal, in the context of
Galactic diffuse emissions at the millimetric and sub-millimetric regimes,
specifically Synchrotron and Thermal Dust emission. Both of them are affected
by contamination at small angular scales due to extra-galactic radio sources
(the former) and to dusty star-forming galaxies (the latter). We consider the
performances of a nearest-neighbors inpainting technique and compare it with
two novels methodologies relying on generative Neural Networks. We show that
the generative network is able to reproduce the statistical properties of the
ground truth signal more consistently with high confidence level. The Python
Inpainter for Cosmological and AStrophysical SOurces (PICASSO) is a package
encoding a suite of inpainting methods described in this work and has been made
publicly available.
Deep convolutional neural networks have been a popular tool for image
generation and restoration. The performance of these networks is related to the
capability of learning realistic features from a large dataset. In this work,
we applied the problem of inpainting non-Gaussian signal, in the context of
Galactic diffuse emissions at the millimetric and sub-millimetric regimes,
specifically Synchrotron and Thermal Dust emission. Both of them are affected
by contamination at small angular scales due to extra-galactic radio sources
(the former) and to dusty star-forming galaxies (the latter). We consider the
performances of a nearest-neighbors inpainting technique and compare it with
two novels methodologies relying on generative Neural Networks. We show that
the generative network is able to reproduce the statistical properties of the
ground truth signal more consistently with high confidence level. The Python
Inpainter for Cosmological and AStrophysical SOurces (PICASSO) is a package
encoding a suite of inpainting methods described in this work and has been made
publicly available.
http://arxiv.org/icons/sfx.gif
