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Predicting the Emergence of Solar Active Regions Using Machine Learning

Published online by Cambridge University Press:  23 December 2024

Spiridon Kasapis Open the ORCID record for Spiridon Kasapis [Opens in a new window] ,
Irina N. Kitiashvili Open the ORCID record for Irina N. Kitiashvili [Opens in a new window] ,
Alexander G. Kosovichev ,
John T. Stefan  and
Bhairavi Apte
Spiridon Kasapis*
Affiliation:
NASA Advanced Supercomputing Division, NASA Ames Research Center, N258, Moffett Field, CA 94035, United States
Irina N. Kitiashvili
Affiliation:
NASA Advanced Supercomputing Division, NASA Ames Research Center, N258, Moffett Field, CA 94035, United States
Alexander G. Kosovichev
Affiliation:
NASA Advanced Supercomputing Division, NASA Ames Research Center, N258, Moffett Field, CA 94035, United States Department of Physics, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, United States
John T. Stefan
Affiliation:
Department of Physics, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, United States
Bhairavi Apte
Affiliation:
Department of Physics, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, United States
*
email: [email protected]
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Abstract

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To create early warning capabilities for upcoming Space Weather disturbances, we have selected a dataset of 61 emerging active regions, which allows us to identify characteristic features in the evolution of acoustic power density to predict continuum intensity emergence. For our study, we have utilized Doppler shift and continuum intensity observations from the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO). The local tracking of 30.66 × 30.66-degree patches in the vicinity of active regions allowed us to trace the evolution of active regions starting from the pre-emergence state. We have developed a machine learning model to capture the acoustic power flux density variations associated with upcoming magnetic flux emergence. The trained Long Short-Term Memory (LSTM) model is able to predict 5 hours ahead whether, in a given area of the solar surface, continuum intensity values will decrease. The performed study allows us to investigate the potential of the machine learning approach to predict the emergence of active regions using acoustic power maps as input.

Keywords

methods: statisticaldata analysisSun: activitysunspots
Type
Contributed Paper
Information
Proceedings of the International Astronomical Union , Volume 19 , Symposium S365: Dynamics of Solar and Stellar Convection Zones and Atmospheres , December 2023 , pp. 311 - 319
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Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of International Astronomical Union

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