%0 Generic %1 glaser2019radiogan %A Glaser, Nina %A Wong, O Ivy %A Schawinski, Kevin %A Zhang, Ce %D 2019 %K clean radioGAN %R 10.1093/mnras/stz1534 %T RadioGAN - Translations between different radio surveys with generative adversarial networks %U http://arxiv.org/abs/1906.03874 %X Radio surveys are widely used to study active galactic nuclei. Radio interferometric observations typically trade-off surface brightness sensitivity for angular resolution. Hence, observations using a wide range of baseline lengths are required to recover both bright small-scale structures and diffuse extended emission. We investigate if generative adversarial networks (GANs) can extract additional information from radio data and might ultimately recover extended flux from a survey with a high angular resolution and vice versa. We use a GAN for the image-to-image translation between two different data sets, namely the Faint Images of the Radio Sky at Twenty-Centimeters (FIRST) and the NRAO VLA Sky Survey (NVSS) radio surveys. The GAN is trained to generate the corresponding image cutout from the other survey for a given input. The results are analyzed with a variety of metrics, including structural similarity as well as flux and size comparison of the extracted sources. RadioGAN is able to recover extended flux density within a $20\%$ margin for almost half of the sources and learns more complex relations between sources in the two surveys than simply convolving them with a different synthesized beam. RadioGAN is also able to achieve subbeam resolution by recognizing complicated underlying structures from unresolved sources. RadioGAN generates over a third of the sources within a $20\%$ deviation from both original size and flux for the FIRST to NVSS translation, while for the NVSS to FIRST mapping it achieves almost $30\%$ within this range.

@misc{glaser2019radiogan, abstract = {Radio surveys are widely used to study active galactic nuclei. Radio interferometric observations typically trade-off surface brightness sensitivity for angular resolution. Hence, observations using a wide range of baseline lengths are required to recover both bright small-scale structures and diffuse extended emission. We investigate if generative adversarial networks (GANs) can extract additional information from radio data and might ultimately recover extended flux from a survey with a high angular resolution and vice versa. We use a GAN for the image-to-image translation between two different data sets, namely the Faint Images of the Radio Sky at Twenty-Centimeters (FIRST) and the NRAO VLA Sky Survey (NVSS) radio surveys. The GAN is trained to generate the corresponding image cutout from the other survey for a given input. The results are analyzed with a variety of metrics, including structural similarity as well as flux and size comparison of the extracted sources. RadioGAN is able to recover extended flux density within a $20\%$ margin for almost half of the sources and learns more complex relations between sources in the two surveys than simply convolving them with a different synthesized beam. RadioGAN is also able to achieve subbeam resolution by recognizing complicated underlying structures from unresolved sources. RadioGAN generates over a third of the sources within a $20\%$ deviation from both original size and flux for the FIRST to NVSS translation, while for the NVSS to FIRST mapping it achieves almost $30\%$ within this range.}, added-at = {2019-06-11T15:47:58.000+0200}, author = {Glaser, Nina and Wong, O Ivy and Schawinski, Kevin and Zhang, Ce}, biburl = {https://www.bibsonomy.org/bibtex/2acf110d0e3b36c579cb9930398293576/heh15}, description = {RadioGAN - Translations between different radio surveys with generative adversarial networks}, doi = {10.1093/mnras/stz1534}, interhash = {1441426e7c8d005d20a8cbfb6feb6b95}, intrahash = {acf110d0e3b36c579cb9930398293576}, keywords = {clean radioGAN}, note = {cite arxiv:1906.03874}, timestamp = {2019-06-11T15:47:58.000+0200}, title = {RadioGAN - Translations between different radio surveys with generative adversarial networks}, url = {http://arxiv.org/abs/1906.03874}, year = 2019 }