Solar Active Regions Emergence Prediction Using Long Short-Term Memory Networks

TL;DR

This study develops LSTM-based machine learning models to predict solar active region emergence 5 to 29 hours in advance using solar observation data, demonstrating promising predictive accuracy and setting a foundation for future ML-aided solar forecasting.

Contribution

The paper introduces novel LSTM models trained on solar observation time-series data to predict active region emergence hours before it occurs, advancing solar activity forecasting methods.

Findings

Successful prediction of AR emergence up to 29 hours in advance.

Average RMSE of 0.11 indicates high prediction accuracy.

Model accurately predicted emergence for all tested active regions.

Abstract

We developed Long Short-Term Memory (LSTM) models to predict the formation of active regions (ARs) on the solar surface. Using the Doppler shift velocity, the continuum intensity, and the magnetic field observations from the Solar Dynamics Observatory (SDO) Helioseismic and Magnetic Imager (HMI), we have created time-series datasets of acoustic power and magnetic flux, which are used to train LSTM models on predicting continuum intensity, 12 hours in advance. These novel machine learning (ML) models are able to capture variations of the acoustic power density associated with upcoming magnetic flux emergence and continuum intensity decrease. Testing of the models' performance was done on data for 5 ARs, unseen from the models during training. Model 8, the best performing model trained, was able to make a successful prediction of emergence for all testing active regions in an experimental setting and three of them in an operational. The model predicted the emergence of AR11726, AR13165, and AR13179 respectively 10, 29, and 5 hours in advance, and variations of this model achieved average RMSE values of 0.11 for both active and quiet areas on the solar disc. This work sets the foundations for ML-aided prediction of solar ARs.