摘要：Characterizing the location of the outer boundary of the outer radiation belt is a key aspect of improving radiation belt models and helps to constrain our understanding of the mechanisms by which the source and seed electron populations are transported into the radiation belts. In this paper, we hypothesize that there are statistical differences in the electron distribution function across the radiation belt outer boundary, and thus analyze electron flux data from the THEMIS (Time History of Events and Macroscale Interactions during Substorms) satellites to identify this location. We validate our hypothesis by using modeled electron L* values to approximately characterize the differences between electron distribution functions inside and outside of the radiation belts. Initially, we perform a simple statistical analysis by studying the radial evolution of the electron distribution functions. This approach does not yield a clear discontinuity, thus highlighting the need for more complex statistical treatment of the data. Subsequently, we employ machine learning (with no dependence on radial position or L*) to test a range of candidate outer boundary locations. By analyzing the performance of the models at each candidate location, we identify a statistical boundary at ≈8 R E , with results suggesting some variability. This statistical boundary is typically further out than those used in current radiation belt models. Plain Language Abstract Earth's magnetic field traps highly energetic particles in a donut shaped region, referred to as “the radiation belts”. Our work focuses on the outer belt, comprised of electrons. Many spacecraft orbit within this region, exposing them to potential damage. To mitigate this, the radiation belts must be understood and modeled. The outer boundary is crucial to modeling, driving changes in radiation belt activity. The boundary is also important because its location helps us to understand which processes form the radiation belts. In this paper, we analyze electron data measured by satellites to identify the location of the radiation belt's outer boundary by using simple statistical methods and machine learning. Our results show that simple statistical methods cannot be used to deduce an outer boundary. Using machine learning, we test many candidate boundary locations and by quantifying the model performances at each of these locations, we are able to identify a statistical boundary location. This boundary is located at approximately eight Earth radii away from the planet, which is typically further out than the boundaries currently used by radiation belt models, although our analysis suggests the boundary location may be variable.
