Paul J. Wright

Over the past 50 years, a variety of instruments have obtained images of the Sun’s magnetic field (magnetograms) to study its origin and evolution. While improvements in instrumentation have led to breakthroughs in our understanding of physical phenomena, differences between subsequent instruments such as resolution, noise, and saturation levels all introduce inhomogeneities into long-term data sets. This poses a significant issue for research applications that require high-resolution and homogeneous data spanning time frames longer than the lifetime of a single instrument.

Peer-reviewed Research Papers

[1] Jungbluth, A., Gitiaux, X., Maloney, S., Shneider, C., Wright, P. J., et al, 2019. Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics & Losses, in 33rd Neural Information Processing Systems (NeurIPS) workshop on Machine Learning in Physical Sciences, Vancouver, Canada, 2019

[2] Gitiaux, X., Maloney, S., Jungbluth, A., Shneider, C., Wright, P. J., et al, 2019. Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties, in 33rd Neural Information Processing Systems (NeurIPS) workshop on Bayesian Deep Learning, Vancouver, Canada, 2019

bold denotes researchers advised/mentored

Google

Google Cloud recently developed a microsite at cloud.withgoogle.com/intel, and produced videos about our work at the Frontier Development Lab (FDL). Check out the video below!

Research Poster

A poster on this work can be downloaded here!