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DeepEM: Demonstrating a Deep Learning Approach to DEM Inversion

Wright, Paul J.; Cheung, Mark C. M.; Thomas, Rajat; Galvez, Richard; Szenicer, Alexandre; Jin, Meng; Muñoz-Jaramillo, Andrés; Fouhey, David

DeepEM is a (supervised) deep learning approach to differential emission measure (DEM) inversion that is currently under development on GitHub.

This first release coincides with the version of DeepEM demonstrated in Chapter 4 of the Machine Learning, Statistics, and Data Mining for Heliophysics e-book (Bobra & Mason 2018). Within the chapter (and the code provided here, DeepEM.ipynb) we demonstrate how a simple implementation of supervised learning can be used to reconstruct DEM maps from SDO/AIA data. Caveats of this simple implementation and future work are also discussed.

The Machine Learning, Statistics, and Data Mining for Heliophysics e-book can be accessed at https://helioml.github.io/HelioML/, and the interactive DeepEM notebook (Chapter 4) is located at https://helioml.github.io/HelioML/04/1/notebook.

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  • Bobra & Mason (2018). HelioML e-book. 10.5281/zenodo.2575738

  • Cheung et al (2015). Thermal Diagnostics with the Atmospheric Imaging Assembly onboard the Solar Dynamics Observatory: A Validated Method for Differential Emission Measure Inversions. 10.1088/0004-637X/807/2/143

  • Hannah & Kontar (2012). Differential emission measures from the regularized inversion of Hinode and SDO data. 10.1051/0004-6361/201117576

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