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This study introduces FlareEUV, a lightweight attention-based deep learning model designed to forecast extreme ultraviolet irradiance during significant solar flares using multi-instrument data from NASA's SDO. By directly correlating magnetic structures with coronal emission in raw imaging data, the framework demonstrates superior short-term prediction capabilities compared to baseline methods for Solar Cycle 24 events. While applied to astrophysics, the core innovation lies entirely within machine learning architectures and multimodal data fusion techniques relevant to AI researchers.
A Deep Learning Framework for Predicting Solar EUV Irradiance During Significant Flares
arXiv:2607.19597v1 Announce Type: cross 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.
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.
Solar and Stellar Astrophysics (astro-ph.SR); Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG)
arXiv:2607.19597 [astro-ph.SR]
(or arXiv:2607.19597v1 [astro-ph.SR] for this version)
https://doi.org/10.48550/arXiv.2607.19597 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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