使用机器学习编译的MMS弓形激波穿越数据库
A database of MMS bow shock crossings compiled using machine learning
摘要：
到目前为止，在航天器发送的数据中识别无碰撞激波交叉是手动完成的。如果冲击物理学家想进行案例研究或统计研究，这是一项乏味的工作。我们使用机器学习方法来自动识别来自磁层多尺度（MMS）航天器的激波交叉。我们编制了一个数据库，包括各种航天器相关的和冲击相关的参数，为每个事件的交叉。此外，我们表明，在数据库中的冲击的属性，在实空间和参数空间都展开。我们还提出了一个可能的科学应用的数据库，通过寻找离子加速效率在冲击和不同的冲击参数，如$θ_{Bn}$和$M_A$之间的相关性。此外，我们统计调查的离子加速效率。我们没有发现明显的加速效率和$M_A$之间的相关性，我们发现，准平行冲击加速离子更有效。
英文摘要：
Identifying collisionless shock crossings in data sent from spacecraft has so far been done manually. It is a tedious job that shock physicists have to go through if they want to conduct case studies or perform statistical studies. We use a machine learning approach to automatically identify shock crossings from the Magnetospheric Multiscale (MMS) spacecraft. We compile a database of those crossings including various spacecraft related and shock related parameters for each event. Furthermore, we show that the shocks in the database have properties that are spread out both in real space and parameter space. We also present a possible science application of the database by looking for correlations between ion acceleration efficiency at shocks and different shock parameters such as $θ_{Bn}$ and $M_A$. Furthermore, we investigate statistically the ion acceleration efficiency. We find no clear correlation between the acceleration efficiency and $M_A$ and we find that quasi-parallel shocks are more efficient at accelerating ions.
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