Predicting the Emergence of Solar Active Regions Using Machine Learning
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912167788281856 |
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| author | Kasapis, Spiridon Kitiashvili, Irina N. Kosovichev, Alexander G. Stefan, John T. Apte, Bhairavi |
| author_facet | Kasapis, Spiridon Kitiashvili, Irina N. Kosovichev, Alexander G. Stefan, John T. Apte, Bhairavi |
| contents | 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 x 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_08890 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Predicting the Emergence of Solar Active Regions Using Machine Learning Kasapis, Spiridon Kitiashvili, Irina N. Kosovichev, Alexander G. Stefan, John T. Apte, Bhairavi Solar and Stellar Astrophysics Machine Learning 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 x 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. |
| title | Predicting the Emergence of Solar Active Regions Using Machine Learning |
| topic | Solar and Stellar Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2402.08890 |