LSTM Networks Predict Solar Active Region Magnetic Flux Evolution

This research focuses on developing machine learning models capable of predicting the evolution of magnetic flux during solar active region (AR) emergence. The models utilize 1D time series of continuum intensity and solar oscillation power maps for 53 active regions and their surrounding quiet-Sun areas. The MagFluxLSTM architecture, implementing a single-stage standard Long-Short Term Memory (LSTM) network, demonstrates superior performance compared to the more complex MagFluxEnc-Dec model. The MagFluxLSTM model can predict magnetic flux emergence 3-10 hours in advance within a 12-hour prediction window in both experimental and operational-type settings for the 5 testing active regions. The early prediction of solar active regions is crucial for operational forecasting systems, as these regions are the primary drivers of space weather events. The ability to accurately forecast the emergence of ARs allows for proactive measures to be taken to mitigate the potential impact of space weather on satellites, power grids, and other critical infrastructure. Further research and development in this area could lead to more sophisticated models that incorporate additional data sources and improve prediction accuracy.

*Transparency Footnote: This analysis was conducted by an AI model and reviewed by human experts. The AI model was trained on a broad range of publicly available scientific literature and adheres to the EU AI Act Article 50 requirements for transparency and explainability.*

_Context: This intelligence report was compiled by the DailyOrbitalWire Strategy Engine. Verified for Art. 50 Compliance._