论文标题
通过基于视频的深度学习进行操作太阳耀斑预测
Operational solar flare forecasting via video-based deep learning
论文作者
论文摘要
操作耀斑的预测旨在提供预测，这些预测通常每天每天都在规模上做出有关耀斑发生的太空天气影响的决策。这项研究表明，当在考虑太阳周期的周期性时生成网络优化的培训和验证集时，可以将基于视频的深度学习用于操作目的。具体而言，本文描述了一种算法，该算法可以应用于建立根据与特定周期相关的耀斑类速率平衡的活动区域集。这些集合用于训练和验证由卷积神经网络和长短记忆网络组合组合的长期卷积网络。在两个预测窗口中，分别包含2015年3月和2017年9月的太阳风暴的情况下，评估了这种方法的可靠性。
Operational flare forecasting aims at providing predictions that can be used to make decisions, typically at a daily scale, about the space weather impacts of flare occurrence. This study shows that video-based deep learning can be used for operational purposes when the training and validation sets used for the network optimization are generated while accounting for the periodicity of the solar cycle. Specifically, the paper describes an algorithm that can be applied to build up sets of active regions that are balanced according to the flare class rates associated to a specific cycle phase. These sets are used to train and validate a Long-term Recurrent Convolutional Network made of a combination of a convolutional neural network and a Long-Short Memory network. The reliability of this approach is assessed in the case of two prediction windows containing the solar storm of March 2015 and September 2017, respectively.
