Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics & Losses
This work addresses the challenge of integrating disparate solar magnetic field data for long-term studies of the solar cycle, which is incremental as it applies existing super-resolution methods with new physics-based constraints.
The authors tackled the problem of combining solar magnetogram data from different instruments by using machine learning to super-resolve low-resolution images and translate between instrument characteristics, introducing physics-based metrics and losses to preserve underlying physics.
Breakthroughs in our understanding of physical phenomena have traditionally followed improvements in instrumentation. Studies of the magnetic field of the Sun, and its influence on the solar dynamo and space weather events, have benefited from improvements in resolution and measurement frequency of new instruments. However, in order to fully understand the solar cycle, high-quality data across time-scales longer than the typical lifespan of a solar instrument are required. At the moment, discrepancies between measurement surveys prevent the combined use of all available data. In this work, we show that machine learning can help bridge the gap between measurement surveys by learning to super-resolve low-resolution magnetic field images and translate between characteristics of contemporary instruments in orbit. We also introduce the notion of physics-based metrics and losses for super-resolution to preserve underlying physics and constrain the solution space of possible super-resolution outputs.