A Deep Learning Framework for Predicting Solar EUV Irradiance During Significant Flares

2026/07/21 by Sathvik Soman, Jason T. L. Wang, Haimin Wang +1
#astro-ph.SR #astro-ph.IM #cs.LG

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Abstract

We present FlareEUV, a multimodal deep learning framework for predicting daily extreme ultraviolet (EUV) irradiance at 6.5 nm over three consecutive days during significant solar flares, using multi-instrument observations from NASA's Solar Dynamics Observatory (SDO). We consider 33 significant flares in the period between 2011 and 2014 in Solar Cycle 24. The SDO observations include 13 co-aligned full-disk images, comprising eight AIA EUV/UV and five HMI magnetic/continuum products. FlareEUV learns the relationship between magnetic structure and coronal emission from the raw imaging data using a lightweight attention-based architecture. Our experimental results demonstrate the good performance of FlareEUV in short-term EUV irradiance forecasting during the significant flares and its superiority over baseline methods.

Citations

• Deep Residual Learning for Image Recognition
• Using SDO’s AIA to investigate energy transport from a flare’s energy release site to the chromosphere

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