# RadioGAN - Translations between different radio surveys with generative adversarial networks

## TL;DR

RadioGAN employs generative adversarial networks to translate radio survey images, effectively recovering extended flux and resolving structures beyond traditional resolution limits, thus enhancing data analysis in radio astronomy.

## Contribution

This work introduces RadioGAN, a novel GAN-based method for translating between different radio survey datasets, improving flux and size recovery beyond standard convolutional approaches.

## Key findings

- RadioGAN recovers extended flux within 20% for nearly half of sources.
- It achieves over a third of sources within 20% deviation in size and flux for FIRST to NVSS translation.
- The method learns complex relations between surveys, surpassing simple convolution models.

## 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.