Argall Matthew R, Small Colin R, Piatt Samantha, Breen Liam, Petrik Marek, Kokkonen Kim, Barnum Julie, Larsen Kristopher, Wilder Frederick D, Oka Mitsuo, Paterson William R, Torbert Roy B, Ergun Robert E, Phan Tai, Giles Barbara L, Burch James L
Space Science Center, EOS, University of New Hampshire, Durham, NC, United States.
Department of Computer Science, University of New Hampshire, Durham, NC, United States.
Front Astron Space Sci. 2020;7:54. doi: 10.3389/fspas.2020.00054. Epub 2020 Sep 1.
Global-scale energy flow throughout Earth's magnetosphere is catalyzed by processes that occur at Earth's magnetopause (MP). Magnetic reconnection is one process responsible for solar wind entry into and global convection within the magnetosphere, and the MP location, orientation, and motion have an impact on the dynamics. Statistical studies that focus on these and other MP phenomena and characteristics inherently require MP identification in their event search criteria, a task that can be automated using machine learning so that more man hours can be spent on research and analysis. We introduce a Long-Short Term Memory (LSTM) Recurrent Neural Network model to detect MP crossings and assist studies of energy transfer into the magnetosphere. As its first application, the LSTM has been implemented into the operational data stream of the Magnetospheric Multiscale (MMS) mission. MMS focuses on the electron diffusion region of reconnection, where electron dynamics break magnetic field lines and plasma is energized. MMS employs automated burst triggers onboard the spacecraft and a Scientist-in-the-Loop (SITL) on the ground to select intervals likely to contain diffusion regions. Only low-resolution survey data is available to the SITL, which is insufficient to resolve electron dynamics. A strategy for the SITL, then, is to select all MP crossings. Of all 219 SITL selections classified as MP crossings during the first five months of model operations, the model predicted 166 (76%) of them, and of all 360 model predictions, 257 (71%) were selected by the SITL. Most predictions that were not classified as MP crossings by the SITL were still MP-like, in that the intervals contained mixed magnetosheath and magnetospheric plasmas. The LSTM model and its predictions are public to ease the burden of arduous event searches involving the MP, including those for EDRs. For MMS, this helps free up mission operation costs by consolidating manual classification processes into automated routines.
贯穿地球磁层的全球尺度能量流动是由发生在地球磁层顶（MP）的过程所催化的。磁重联是太阳风进入磁层并在其中进行全球对流的一个过程，磁层顶的位置、方向和运动对动力学有影响。专注于这些以及其他磁层顶现象和特征的统计研究在其事件搜索标准中本质上需要识别磁层顶，这项任务可以通过机器学习自动化，以便将更多的人工时间用于研究和分析。我们引入了一种长短期记忆（LSTM）循环神经网络模型来检测磁层顶穿越，并协助研究能量向磁层的转移。作为其首次应用，LSTM已被应用于磁层多尺度（MMS）任务的运行数据流中。MMS专注于重联的电子扩散区域，在那里电子动力学打破磁力线并使等离子体获得能量。MMS在航天器上采用自动爆发触发装置，并在地面上采用科学家参与（SITL）方式来选择可能包含扩散区域的时间间隔。SITL只能获取低分辨率的探测数据，这不足以解析电子动力学。因此，SITL的一种策略是选择所有磁层顶穿越。在模型运行的前五个月中，在所有被分类为磁层顶穿越的219次SITL选择中，该模型预测了其中的166次（76%），而在所有360次模型预测中，有257次（71%）被SITL选中。大多数未被SITL分类为磁层顶穿越的预测仍然类似磁层顶，因为这些时间间隔包含混合的磁鞘和磁层等离子体。LSTM模型及其预测是公开的，以减轻涉及磁层顶的艰巨事件搜索负担，包括那些针对电子扩散区域的搜索。对于MMS来说，这有助于通过将手动分类过程整合到自动化程序中来节省任务运营成本。
