A deep learning framework for instrument-to-instrument translation of solar observation data

Abstract

The constant improvement of astronomical instrumentation provides the foundation for scientific discoveries. In general, these improvements have only implications forward in time, while previous observations do not benefit from this trend, and the joint use of data sets from different instruments is typically limited by differences in calibration and quality. We provide a deep learning framework for Instrument-To-Instrument translation of solar observation data, enabling homogenized data series of multi-instrument data sets. This is achieved by unpaired domain translations with Generative Adversarial Networks, which eliminate the need for spatial or temporal overlap to relate instruments. We demonstrate that the available data sets can directly profit from instrumental improvements, by applying our method to four different applications of ground- and space-based solar observations. We obtain a homogenized data series of 24 years of space-based observations of the solar EUV corona and line-of-sight magnetic field, solar full-disk observations with increased spatial resolution, real-time mitigation of atmospheric degradations in ground-based observations, and unsigned magnetic field estimates from the solar far-side based on EUV imagery. The direct comparison to simultaneous high-quality observations shows that our method produces images that are perceptually similar, and enables more homogeneous multi-instrument data sets without the requirement of spatial or temporal alignment.