PRESOL: a web-based computational setting for feature-based flare forecasting
Fuente:
arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
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| _version_ | 1866912623022309376 |
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| author | Curletto, Chiara Massa, Paolo Tagliafico, Valeria Campi, Cristina Benvenuto, Federico Piana, Michele Tacchino, Andrea |
| author_facet | Curletto, Chiara Massa, Paolo Tagliafico, Valeria Campi, Cristina Benvenuto, Federico Piana, Michele Tacchino, Andrea |
| contents | Solar flares are the most explosive phenomena in the solar system and the main trigger of the events' chain that starts from Coronal Mass Ejections and leads to geomagnetic storms with possible impacts on the infrastructures at Earth. Data-driven solar flare forecasting relies on either deep learning approaches, which are operationally promising but with a low explainability degree, or machine learning algorithms, which can provide information on the physical descriptors that mostly impact the prediction. This paper describes a web-based technological platform for the execution of a computational pipeline of feature-based machine learning methods that provide predictions of the flare occurrence, feature ranking information, and assessment of the prediction performances. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_01799 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PRESOL: a web-based computational setting for feature-based flare forecasting Curletto, Chiara Massa, Paolo Tagliafico, Valeria Campi, Cristina Benvenuto, Federico Piana, Michele Tacchino, Andrea Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics Machine Learning Space Physics 68T07, 85-04 Solar flares are the most explosive phenomena in the solar system and the main trigger of the events' chain that starts from Coronal Mass Ejections and leads to geomagnetic storms with possible impacts on the infrastructures at Earth. Data-driven solar flare forecasting relies on either deep learning approaches, which are operationally promising but with a low explainability degree, or machine learning algorithms, which can provide information on the physical descriptors that mostly impact the prediction. This paper describes a web-based technological platform for the execution of a computational pipeline of feature-based machine learning methods that provide predictions of the flare occurrence, feature ranking information, and assessment of the prediction performances. |
| title | PRESOL: a web-based computational setting for feature-based flare forecasting |
| topic | Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics Machine Learning Space Physics 68T07, 85-04 |
| url | https://arxiv.org/abs/2510.01799 |