arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.13287astro-ph.SR
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用卷积神经网络分析AIA耀斑观测

Analysing AIA Flare Observations using Convolutional Neural Networks

Teri Love, Thomas Neukirch, Clare E. Parnell

摘要:

为了有效分析太阳能空间飞行任务和地面仪器产生的大量数据,决策树、支持向量机和神经网络等现代机器学习技术可能非常有用。在本文中,我们提出了使用卷积神经网络(CNN)的初步结果,以分析从大气成像组件(AIA)在1600 A波长的观测。对数据进行预处理,以定位观测中可见耀斑带的耀斑区域。创建并训练CNN,通过识别每张图像是否属于四个类别之一,自动分析耀斑带的形状和位置:双带耀斑、紧凑/圆形带状耀斑、边缘耀斑或安静的太阳,最后一个类别充当包含在训练或测试集中的任何数据的控制,其中不存在耀斑区域。所创建的网络可以将耀斑带状观测分类为四个类别中的任何一个,最终精度为94%。初步结果表明,大多数的图像是正确的分类与紧凑的耀斑类是唯一的类,准确度下降到90%以下广告的一些意见被错误地归类为属于肢体类。

英文摘要:

In order to efficiently analyse the vast amount of data generated by solar space missions and ground-base instruments, modern machine learning techniques such as decision trees, support vector machines (SVMs) and neural networks can be very useful. In this paper we present initial results from using a convolutional neural network (CNN) to analyse observations from the Atmospheric Imaging Assembly (AIA) in the 1600A wavelength. The data is pre-processed to locate flaring regions where flare ribbons are visible in the observations. The CNN is created and trained to automatically analyse the shape and position of the flare ribbons, by identifying whether each image belongs into one of four classes: two-ribbon flare, compact/circular ribbon flare, limb flare or quiet Sun, with the final class acting as a control for any data included in the training or test sets where flaring regions are not present. The network created can classify flare ribbon observations into any of the four classes with a final accuracy of 94%. Initial results show that most of the images are correctly classified with the compact flare class being the only class where accuracy drops below 90% ad some observations are wrongly classified as belonging to the limb class.

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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