PRESOL: a web-based computational setting for feature-based flare forecasting
Chiara Curletto, Paolo Massa, Valeria Tagliafico, Cristina Campi, Federico Benvenuto, Michele Piana, Andrea Tacchino
太阳耀斑是太阳系中最具爆炸性的现象,也是从日冕物质抛射开始的事件链的主要触发因素,并导致地磁风暴,可能对地球上的基础设施产生影响。 数据驱动的太阳耀斑预测依赖于深度学习方法,这种方法在操作上很有希望,但具有低可解释性程度,或机器学习算法,它可以提供有关主要影响预测的物理描述符的信息。 本文介绍了一个基于Web的技术平台,用于执行基于特征的机器学习方法的计算管道,提供对耀斑发生的预测,特征排名信息以及预测性能的评估。
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 p...