Radial interplanetary fields at Comets and Mars

太阳系中的物体嵌入太阳风中，它们与太阳风的相互作用取决于它们的大气和磁场。没有全球磁场的天体，如火星和彗星，会形成感应磁层。这个地区是一个理想的等离子体实验室，由不同的离子物种和尘埃颗粒组成，可以用航天器进行探测。了解环境对人类未来的探索和对太阳系的了解至关重要。决定相互作用区形状的主要参数之一是行星际磁场(IMF)的方向。大多数模型假定领域有统一的“标准”方向，这往往会给出好的结果。然而，IMF是高度可变的，文献中没有涵盖很大一部分情况。最近在火星和67P/Churyumov-Gerasimenko彗星上的等离子体观测显示了标准模型没有涵盖的异常波和粒子活动，并将其归因于径向IMF构型。在这些情况下，环境可能会完全改变，离子逃逸和加速的速度也会截然不同。这对行星大气模型和彗尾观测产生了影响，但人们对此知之甚少。在即将到来的任务彗星拦截器遇到彗星并首次在这种环境中进行多点测量之前，这是一个应该填补的知识空白。了解这些特殊条件下的环境的关键是将现有的数值和分析模型与观测结果联系起来，并建立更好地描述非标准情况的新模型。

Objects in the solar system are embedded in the solar wind and how they interact with it depends on their atmosphere and magnetic fields. Bodies without a global magnetic field, like Mars and comets, develop an induced magnetosphere. This region is an ideal plasma laboratory consisting of different ion species and dust particles that can be explored by spacecraft. Understanding the environment is crucial for future human exploration and our understanding of the solar system.One of the main parameters determining the shape of the interaction region is the direction of the interplanetary magnetic field (IMF). Most models assume a unified "standard" direction of the field, which often gives good results. However, the IMF is highly variable, and a large part of cases is not covered in literature.Plasma observations at both Mars and comet 67P/Churyumov-Gerasimenko recently showed unusual wave and particle activity that was not covered by the standard models, and which were attributed to a radial IMF configuration. For these cases the environment can change completely, and ion escape and acceleration rates are dramatically different. This has consequences for planetary atmospheric models and cometary tail observations that are poorly understood. This is a knowledge gap that should be filled before the upcoming mission Comet Interceptor will encounter a comet and take multi-point measurements in this environment for the first time.The key to understanding the environment under these special conditions is to relate existing numerical and analytical models to the observations and build new models that better describe the non-standard cases.