Instrument-To-Instrument translation: Instrumental advances drive restoration of solar observation series via deep learning

TL;DR

This paper introduces a deep learning method for translating between different solar observation instruments, enabling enhanced data quality, continuity, and historical series by leveraging recent instrumental improvements.

Contribution

The authors develop a novel Instrument-To-Instrument translation (ITI) deep learning approach that improves solar observation data across various instruments and time periods.

Findings

Achieved unprecedented spatial resolution in solar full-disk observations.

Created a 24-year homogeneous data series of solar EUV and magnetic field observations.

Enabled real-time atmospheric degradation mitigation in ground-based solar observations.

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. Here we provide a general deep learning method that translates between image domains of different instruments (Instrument-To-Instrument translation; ITI). We demonstrate that the available data sets can directly profit from the most recent instrumental improvements, by applying our method to five different applications of ground- and space-based solar observations. We obtain 1) solar full-disk observations with unprecedented spatial resolution, 2) a homogeneous data series of 24 years of space-based observations of the solar EUV corona and magnetic field, 3) real-time mitigation of atmospheric degradations in ground-based observations, 4) a uniform series of ground-based H$\alpha$ observations starting from 1973, 5) 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 match the reference image distribution.