MMS SITL Ground Loop: Automating the Burst Data Selection Process.

Matthew R Argall,Robert E Ergun,Mitsuo Oka,Kim Kokkonen,Liam Breen,Kristopher Larsen,Colin R Small,James L Burch,William R Paterson,Samantha Piatt,Marek Petrik,Roy B Torbert,Tai Phan,Julie Barnum,Barbara L Giles,Frederick D Wilder

doi:10.3389/fspas.2020.00054

Abstract

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.

Highlights

Earth is a strongly magnetized planet whose internal dynamics are largely influenced by its interaction with the solar wind and the resulting cycle of magnetic reconnection (Dungey, 1961)
Panels 5 and 6 show similarities and differences between the time intervals and Figure of Merit (FOM) values of selections made by the SITL and those made by the automated burst system (ABS) and Ground Loop System (GLS)
A burst management system consisting of the Scientistin-the-Loop (SITL), Automated Burst System, and Ground Loop System (GLS) ensure that the right ∼4% of data makes it to the ground

Summary

Introduction

Earth is a strongly magnetized planet whose internal dynamics are largely influenced by its interaction with the solar wind and the resulting cycle of magnetic reconnection (Dungey, 1961). Reconnection occurs initially at the magnetopause (MP), at the interface between the shocked solar wind and Earth’s magnetosphere (MSP), in what is known as the electron diffusion region (EDR). The EDR had been enigmatic, with few direct observations (Nagai et al, 2011, 2013; Scudder et al, 2012; Tang et al, 2013; Oka et al, 2016) because spacecraft lacked the spacial and temporal resolution to resolve electron-scale dynamics. These limitations were overcome by the Magnetospheric Multiscale (MMS) mission. MMS has identified more than 50 EDRs (see Webster et al, 2018 for a partial list) and greatly expanded our knowledge of what catalyzes the global reconnection cycle

Results

Discussion

Conclusion