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

• URL: http://arxiv.org/abs/2409.17421v1
• Date: Wed, 25 Sep 2024 23:09:46 GMT
• Title: Solar Active Regions Emergence Prediction Using Long Short-Term Memory Networks
• Authors: Spiridon Kasapis, Irina N. Kitiashvili, Alexander G. Kosovichev, John T. Stefan
• Abstract summary: We develop Long Short-Term Memory (LSTM) models to predict the formation of active regions (ARs) on the solar surface. Time-series datasets of acoustic power and magnetic flux 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.
• Score: 44.99833362998488
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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