SolARED: Solar Active Region Emergence Dataset for Machine Learning Aided Predictions
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866915739097628672 |
|---|---|
| author | Kasapis, Spiridon Dogan, Eren Kitiashvili, Irina N. Kosovichev, Alexander G. Stefan, John T. Butler, Jake D. Tirona, Jonas Patil, Sarang Xu, Mengjia |
| author_facet | Kasapis, Spiridon Dogan, Eren Kitiashvili, Irina N. Kosovichev, Alexander G. Stefan, John T. Butler, Jake D. Tirona, Jonas Patil, Sarang Xu, Mengjia |
| contents | The development of accurate forecasts of solar eruptive activity has become increasingly important for preventing potential impacts on space technologies and exploration. Therefore, it is crucial to detect Active Regions (ARs) before they start forming on the solar surface. This will enable the development of early-warning capabilities for upcoming space weather disturbances. For this reason, we prepared the Solar Active Region Emergence Dataset (SolARED). The dataset is derived from full-disk maps of the Doppler velocity, magnetic field, and continuum intensity, obtained by the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO). SolARED includes time series of remapped, tracked, and binned data that characterize the evolution of acoustic power of solar oscillations, unsigned magnetic flux, and continuum intensity for 50 large ARs before, during, and after their emergence on the solar surface, as well as surrounding areas observed on the solar disc between 2010 and 2023. The resulting ML-ready SolARED dataset is designed to support enhancements of predictive capabilities, enabling the development of operational forecasts for the emergence of active regions. The SolARED dataset is available at https://sun.njit.edu/sarportal/, through an interactive visualization web application. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_13145 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SolARED: Solar Active Region Emergence Dataset for Machine Learning Aided Predictions Kasapis, Spiridon Dogan, Eren Kitiashvili, Irina N. Kosovichev, Alexander G. Stefan, John T. Butler, Jake D. Tirona, Jonas Patil, Sarang Xu, Mengjia Solar and Stellar Astrophysics Machine Learning The development of accurate forecasts of solar eruptive activity has become increasingly important for preventing potential impacts on space technologies and exploration. Therefore, it is crucial to detect Active Regions (ARs) before they start forming on the solar surface. This will enable the development of early-warning capabilities for upcoming space weather disturbances. For this reason, we prepared the Solar Active Region Emergence Dataset (SolARED). The dataset is derived from full-disk maps of the Doppler velocity, magnetic field, and continuum intensity, obtained by the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO). SolARED includes time series of remapped, tracked, and binned data that characterize the evolution of acoustic power of solar oscillations, unsigned magnetic flux, and continuum intensity for 50 large ARs before, during, and after their emergence on the solar surface, as well as surrounding areas observed on the solar disc between 2010 and 2023. The resulting ML-ready SolARED dataset is designed to support enhancements of predictive capabilities, enabling the development of operational forecasts for the emergence of active regions. The SolARED dataset is available at https://sun.njit.edu/sarportal/, through an interactive visualization web application. |
| title | SolARED: Solar Active Region Emergence Dataset for Machine Learning Aided Predictions |
| topic | Solar and Stellar Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2601.13145 |