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arXiv 2108.08925physics.space-phphysics.data-an
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航天器测量中识别和分类等离子体波的新型机器学习技术

A novel machine learning technique to identify and categorize plasma waves in spacecraft measurements

Daniel Vech, David M. Malaspina

摘要:

从地球磁层、太阳风和行星磁层获得的磁场数据超过$10^6$小时。在这些大型数据集中识别等离子体波是一个耗时且繁琐的过程。在本文中,我们提出了一个解决这个问题的方法。我们演示了如何自组织映射可用于快速数据减少和识别等离子体波在大数据集。我们使用来自磁层多尺度任务(MMS $_1$)的72,000个磁通门和110,000个搜索线圈磁场功率谱,并展示了自组织映射如何根据功率谱的形状将其分类。以这种方式组织数据使得识别具有相似属性的功率谱非常简单,因此这种技术大大减少了对数据的手动检查的需要。我们认为,自组织映射提供了一个有效的时间和强大的技术,它可以显着加快处理磁场数据和发现新的波形。

英文摘要:

The available magnetic field data from the terrestrial magnetosphere, solar wind and planetary magnetospheres exceeds over $10^6$ hours. Identifying plasma waves in these large data sets is a time consuming and tedious process. In this Paper, we propose a solution to this problem. We demonstrate how Self-Organizing Maps can be used for rapid data reduction and identification of plasma waves in large data sets. We use 72,000 fluxgate and 110,000 search coil magnetic field power spectra from the Magnetospheric Multiscale Mission (MMS$_1$) and show how the Self-Organizing Map sorts the power spectra into groups based on their shape. Organizing the data in this way makes it very straightforward to identify power spectra with similar properties and therefore this technique greatly reduces the need for manual inspection of the data. We suggest that Self-Organizing Maps offer a time effective and robust technique, which can significantly accelerate the processing of magnetic field data and discovery of new wave forms.

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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