论文标题
太阳耀斑的可靠概率预测：深耀斑净可靠（DEFN-R）
Reliable Probability Forecast of Solar Flares: Deep Flare Net-Reliable (DeFN-R)
论文作者
论文摘要
我们使用深层神经网络（称为Deep Flare Net-Coclible（Defn-R））开发了可靠的概率太阳耀斑预测模型。该模型可以预测观察图像后24小时内发生的最大耀斑以及事件的发生概率。我们检测到2010 - 2015年在2015年通过太阳能天文台拍摄的3x10^5太阳图像的活动区域，并为每个区域提取了79个特征，我们用X-，M-和C类的耀斑出现标签注释。提取的特征与Nishizuka等人使用的特征相同。 （2018）;例如，光球中的视线/矢量磁图在图像前的X射线发射率和X射线发射率1和2小时。我们将数据库的时间顺序分为两个，以在操作环境中进行培训和测试：2010 - 2014年的数据集进行培训，而2015年进行了测试。 defn-r由由批处理正常化和跳过连接形成的多层感知器组成。通过调整优化方法，训练了DEFN-R以优化Brier技能得分（BSS）。结果，我们通过改善可靠性图，在保持相对工作特性曲线几乎相同的同时，通过改善可靠性图来实现> = C级耀斑预测的BS = 0.41和0.30的BSS = 0.41。请注意，为确定性预测进行了优化，该预测的归一化阈值为50％。另一方面，根据观察事件速率进行了概率预测，对Defn-R进行了优化，该概率可以根据用户的目的选择其概率阈值。
We developed a reliable probabilistic solar flare forecasting model using a deep neural network, named Deep Flare Net-Reliable (DeFN-R). The model can predict the maximum classes of flares that occur in the following 24 h after observing images, along with the event occurrence probability. We detected active regions from 3x10^5 solar images taken during 2010-2015 by Solar Dynamic Observatory and extracted 79 features for each region, which we annotated with flare occurrence labels of X-, M-, and C-classes. The extracted features are the same as used by Nishizuka et al. (2018); for example, line-of-sight/vector magnetograms in the photosphere, brightening in the corona, and the X-ray emissivity 1 and 2 h before an image. We adopted a chronological split of the database into two for training and testing in an operational setting: the dataset in 2010-2014 for training and the one in 2015 for testing. DeFN-R is composed of multilayer perceptrons formed by batch normalizations and skip connections. By tuning optimization methods, DeFN-R was trained to optimize the Brier skill score (BSS). As a result, we achieved BSS = 0.41 for >=C-class flare predictions and 0.30 for >=M-class flare predictions by improving the reliability diagram while keeping the relative operating characteristic curve almost the same. Note that DeFN is optimized for deterministic prediction, which is determined with a normalized threshold of 50%. On the other hand, DeFN-R is optimized for a probability forecast based on the observation event rate, whose probability threshold can be selected according to users' purposes.
