论文标题

深度学习的应用和解释以识别出现前磁场模式

Application and interpretation of deep learning for identifying pre-emergence magnetic-field patterns

论文作者

Dhuri, Dattaraj B., Hanasoge, Shravan M., Birch, Aaron C., Schunker, Hannah

论文摘要

在太阳能内部产生的磁通量出现到表面,形成活跃区域(ARS)和黑子。通量的出现可能会触发爆炸性事件,例如耀斑和冠状质量弹出,因此理解出现对于空间天气预测很有用。任何出现前签名的证据也将阐明造成出现的次表面过程。在本文中,我们使用深度卷积神经网络(CNN)对太阳动力学天文台/热震新的活性区域(SDO/HEAR)数据集(SDO/HEAR)数据集(SDO/HEAR)数据集(CNN)进行了首次分析,以表征前射精前表面磁场特性。受过训练的CNN分类在出现线图(PE)磁性图和一组对照组的非出现磁力图(NE)磁力图中,真正的技能统计量(TSS)得分约为85%,出现前3H和出现前的24h,〜40 \%,〜40 \%。我们的结果比仅使用无符号磁通量的判别分析获得的基线分类TSS更好。我们开发了一种网络修剪算法来解释训练有素的CNN,并表明CNN融合了对磁力图的未签名磁通量呈积极响应和负面响应的过滤器。使用合成磁图,我们证明了CNN输出对具有小规模和强烈场的磁区域的长度尺度敏感,该磁区域可产生最大的CNN输出,并且可能是特征性的前出现图。鉴于深度学习的越来越流行,此处开发的技术用于解释受过训练的CNN(使用网络修剪和合成数据)与未来在太阳能和天体物理数据分析中的应用有关。

Magnetic flux generated within the solar interior emerges to the surface, forming active regions (ARs) and sunspots. Flux emergence may trigger explosive events, such as flares and coronal mass ejections and therefore understanding emergence is useful for space-weather forecasting. Evidence of any pre-emergence signatures will also shed light on sub-surface processes responsible for emergence. In this paper, we present a first analysis of emerging ARs from the Solar Dynamics Observatory/Helioseismic Emerging Active Regions (SDO/HEAR) dataset (Schunker et al. 2016) using deep convolutional neural networks (CNN) to characterize pre-emergence surface magnetic-field properties. The trained CNN classifies between pre-emergence (PE) line-of-sight magnetograms and a control set of non-emergence (NE) magnetograms with a True Skill Statistic (TSS) score of ~85%, 3h prior to emergence and ~40\%, 24h prior to emergence. Our results are better than a baseline classification TSS obtained using discriminant analysis of only the unsigned magnetic flux. We develop a network pruning algorithm to interpret the trained CNN and show that the CNN incorporates filters that respond positively as well as negatively to the unsigned magnetic flux of the magnetograms. Using synthetic magnetograms, we demonstrate that the CNN output is sensitive to the length-scale of the magnetic regions with small-scale and intense fields producing maximum CNN output and possibly a characteristic pre-emergence pattern. Given increasing popularity of deep learning, techniques developed here for interpretation of the trained CNN -- using network pruning and synthetic data -- are relevant for future applications in solar and astrophysical data analysis.

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