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Using Long-Short Term Memory Models to Predict Solar Active Regions EmergenceWe train Long Short-Term Memory (LSTM) models that predict the formation of active regions (ARs), the main source of eruptive solar activity. Using the Doppler shift velocity, the continuum intensity and the magnetic field full-disk maps from SDO/HMI we have created time-series datasets of acoustic power and magnetic flux which are used to train Long Short-Term Memory (LSTM) models on predicting decreases in continuum intensity 12 hours in advance. Testing of the models' performance was done on data from 5 ARs, unseen from the model during training. The model predicted the emergence of AR11726, AR13165 and AR13179, 10, 29 and 5 hours in advance, and variations of this model achieved average RMSE values of 0.11 for both active and quiet parts of the solar disc, showing the ability of the model to capture acoustic power anomalies and predict continuum intensity variations. This work sets the foundations for the very first ML-aided prediction of solar ARs.
Document ID
20240009048
Acquisition Source
Ames Research Center
Document Type
Poster
Authors
Spiridon Kasapis
(Oak Ridge Associated Universities Oak Ridge, United States)
Irina Kitiashvili
(Ames Research Center Mountain View, United States)
Alexander G Kosovichev
(New Jersey Institute of Technology Newark, United States)
John T Stefan
(New Jersey Institute of Technology Newark, United States)
Bhairavi Apte
(New Jersey Institute of Technology Newark, United States)
Date Acquired
July 16, 2024
Subject Category
Aeronautics (General)
Meeting Information
Meeting: XXXII IAU General Assembly 2024
Location: Cape Town
Country: ZA
Start Date: August 6, 2024
End Date: August 15, 2024
Sponsors: International Astronomical Union
Funding Number(s)
WBS: 791926.02.09.02.03
Distribution Limits
Public
Copyright
Portions of document may include copyright protected material.
Technical Review
NASA Peer Committee
Keywords
SMD
Heliophysics
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