Machine Learning for Space Weather: Solar Flare Forecasting Using SDO/HMI Magnetogram Time Series

Solar flares, sudden bursts of electromagnetic energy originating from magnetically active regions on the solar surface, pose significant risks to satellite infrastructure, communication systems, and power grids. Accurate forecasting of these events is crucial for advancing space weather prediction and safeguarding technological infrastructure. The interconnected nature of the Sun's atmospheric layers—from the corona to the lower photosphere—highlights the need for comprehensive data analysis techniques that leverage modern advancements in machine learning (ML) and physically informed models.

Traditional approaches have relied on features extracted from line-of-sight (LoS) magnetograms of solar active regions, historically linked to increased flare activity. However, recent studies employing LoS magnetogram time series have shown limited improvements, prompting the need for novel methodologies that integrate learning-based and physics-based insights.

To address this challenge, we present a deep learning-based framework for solar flare forecasting, leveraging the Solar Dynamics Observatory’s Helioseismic and Magnetic Imager (SDO/HMI) LoS magnetograms. Our model frames flare forecasting as a binary time series classification problem, aiming to distinguish active regions likely to produce M- or X-class flares within a 24-hour window. The approach integrates a Convolutional Neural Network (CNN) autoencoder for feature extraction and a Long Short-Term Memory (LSTM) binary classifier for flare activity prediction, achieving a 90% test accuracy.

By leveraging advanced ML techniques, this methodology demonstrates the potential of data-driven models in heliophysics. Our results highlight the transformative role of AI-powered science in advancing solar flare prediction and contributing to the development of reliable early warning systems for space weather forecasting.