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arXiv 1805.03421astro-ph.SR
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用于太阳耀斑预测的深耀斑网络（DeFN）模型

Deep Flare Net (DeFN) model for solar flare prediction

Naoto Nishizuka, Komei Sugiura, Yuki Kubo, Mitsue Den, Mamoru Ishii

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24 pages, 7 figures, 3 tables, Accepted for publication in ApJ
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摘要：

我们使用深度神经网络（DNN）开发了一个太阳耀斑预测模型，称为DeFN。该模型可以计算每个活动区域在接下来的24小时内发生耀斑的概率，该概率用于通过二元分类来确定最可能的最大耀斑类别（例如，&gt;=M类对<M class or >=C类对&lt;C类）。从太阳动力学观测站2010-2015年拍摄的3x10^5张观测图像中，我们自动检测了太阳黑子，并计算了每个区域的79个特征，并附上了X级，M级和C级耀斑发生标签。我们采用了Nishizuka等人（2017）中使用的特征，并添加了一些用于业务预测的特征：131 A（T&gt;=10^7 K）的日冕热增亮以及图像前1和2小时的X射线和131 A强度数据。为了进行操作评估，我们将数据库分为两部分进行训练和测试：2010-2014年的数据集用于训练，2015年的数据集用于测试。DeFN模型由深层多层神经网络组成，通过调整跳跃连接和批量归一化形成。为了统计预测耀斑，训练DeFN模型以优化技能得分，即，真实技能统计（TSS）。结果，我们成功地预测了≥ M级耀斑的TSS=0.80，≥ C级耀斑的TSS=0.63。请注意，在通常的DNN模型中，预测过程是一个黑盒。然而，在DeFN模型中，特征是手动选择的，并且可以在评估后分析哪些特征对预测有效。

英文摘要：

We developed a solar flare prediction model using a deep neural network (DNN), named Deep Flare Net (DeFN). The model can calculate the probability of flares occurring in the following 24 h in each active region, which is used to determine the most likely maximum classes of flares via a binary classification (e.g., >=M class versus <M class or >=C class versus <C class). From 3x10^5 observation images taken during 2010-2015 by Solar Dynamic Observatory, we automatically detected sunspots and calculated 79 features for each region, to which flare occurrence labels of X-, M-, and C-class were attached. We adopted the features used in Nishizuka et al. (2017) and added some features for operational prediction: coronal hot brightening at 131 A (T>=10^7 K) and the X-ray and 131 A intensity data 1 and 2 h before an image. For operational evaluation, we divided the database into two for training and testing: the dataset in 2010-2014 for training and the one in 2015 for testing. The DeFN model consists of deep multilayer neural networks, formed by adapting skip connections and batch normalizations. To statistically predict flares, the DeFN model was trained to optimize the skill score, i.e., the true skill statistic (TSS). As a result, we succeeded in predicting flares with TSS=0.80 for >=M-class flares and TSS=0.63 for >=C-class flares. Note that in usual DNN models, the prediction process is a black box. However, in the DeFN model, the features are manually selected, and it is possible to analyze which features are effective for prediction after evaluation.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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