3 research outputs found

    Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics \& Losses

    Get PDF
    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 \textbf{super-resolve} low-resolution magnetic field images and \textbf{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

    Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties

    Get PDF
    Machine learning techniques have been successfully applied to super-resolution tasks on natural images where visually pleasing results are sufficient. However in many scientific domains this is not adequate and estimations of errors and uncertainties are crucial. To address this issue we propose a Bayesian framework that decomposes uncertainties into epistemic and aleatoric uncertainties. We test the validity of our approach by super-resolving images of the Sun's magnetic field and by generating maps measuring the range of possible high resolution explanations compatible with a given low resolution magnetogram

    Super-Resolution Maps of the Solar Magnetic Field Covering 40 Years of Space Weather Events

    No full text
    As modern society becomes increasingly dependent on technology, space weather events will have a farther-reaching impact than ever before. For nearly 10 years, NASA's Solar Dynamics Observatory (SDO) has continuously monitored the Sun, however, the SDO-era coincides with the weakest solar cycle of the last century: over the last 40 years, there have been nearly 500 X-class solar flares—around 10 times the number of events observed by SDO alone. It is also clear that there is no single observational survey with sufficient time coverage to enable an effective deep learning space weather forecasting application. Crucially, over the past 40 years, numerous observatories have monitored the Sun's magnetic field. However, cross calibrating magnetograms is a complex and non-trivial endeavour as the relationship between observed pixels is strongly affected by a wide range of systematics. Here we present a deep learning application that can convert magnetograms to a target survey while preserving the features and systematics of the target survey. We will first present our approach for upscaling and cross-calibrating images obtained by the Michelson Doppler Imager (MDI; on-board the Solar and Heliospheric Observatory, SOHO), to the resolution of the Helioseismic and Magnetic Imager (SDO/HMI). We will discuss the physics-based metrics, deep learning architectures, and the lessons learned along the way. This work was performed at NASA’s Frontier Development Laboratory (FDL), a public-private partnership to apply AI techniques to accelerate space science discovery and exploration
    corecore