论文标题
使用机器学习编译的MMS弓冲击交叉数据库
A database of MMS bow shock crossings compiled using machine learning
论文作者
论文摘要
到目前为止，已经手动完成了从航天器发送的数据中识别无碰撞冲击交叉点。如果要进行案例研究或进行统计研究，那是一项乏味的工作，即冲击物理学家必须经历。我们使用机器学习方法自动从磁层多尺度（MMS）航天器中识别冲击交叉。我们编译了这些交叉口的数据库，包括每个事件的各种航天器相关和相关参数。此外，我们表明数据库中的冲击具有在真实空间和参数空间中均分布的属性。我们还通过寻找冲击的离子加速度效率与不同的冲击参数（例如$θ_{bn} $和$ m_a $之间的相关性，我们还提出了数据库的科学应用。此外，我们从统计学上研究了离子加速度效率。我们发现加速度效率与$ M_A $之间没有明确的相关性，我们发现准平行冲击在加速离子方面更有效。
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.
