Extreme Solar Flare Prediction Using Residual Networks with HMI Magnetograms and Intensitygrams

TL;DR

This paper introduces a deep learning model using Residual Networks and HMI solar data to accurately predict extreme solar flares, enhancing space weather forecasting capabilities.

Contribution

It presents a novel ResNet-based approach utilizing HMI magnetograms and intensitygrams for improved extreme solar flare prediction.

Findings

High accuracy in classifying extreme flares

HMI magnetograms outperform other SDO AIA images

Magnetic field features are critical for prediction

Abstract

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.