Extreme Solar Flare Prediction Using Residual Networks with HMI Magnetograms and Intensitygrams
This work addresses solar flare prediction to mitigate risks to satellite operations and power grids, representing an incremental advancement in space weather forecasting.
The paper tackled predicting extreme solar flares by using HMI intensitygrams and magnetograms to train a Residual Network, achieving high accuracy and showing that magnetograms provide more useful data than other images for deep learning.
Solar flares, especially C, M, and X class, pose significant risks to satellite operations, communication systems, and power grids. We present a novel approach for predicting extreme solar flares using HMI intensitygrams and magnetograms. By detecting sunspots from intensitygrams and extracting magnetic field patches from magnetograms, we train a Residual Network (ResNet) to classify extreme class flares. Our model demonstrates high accuracy, offering a robust tool for predicting extreme solar flares and improving space weather forecasting. Additionally, we show that HMI magnetograms provide more useful data for deep learning compared to other SDO AIA images by better capturing features critical for predicting flare magnitudes. This study underscores the importance of identifying magnetic fields in solar flare prediction, marking a significant advancement in solar activity prediction with practical implications for mitigating space weather impacts.