Original Research ARTICLE
MMS SITL Ground Loop: Automating the burst data selection process
- 1University of New Hampshire, United States
- 2Cornell University, United States
- 3Laboratory for Atmospheric and Space Physics, University of Colorado Boulder, United States
- 4University of California, Berkeley, United States
- 5Southwest Research Institute (SwRI), United States
- 6Goddard Space Flight Center, National Aeronautics and Space Administration, United States
- 7Southwest Research Institute (SwRI), United States
Global-scale energy flow throughout Earth’s magnetosphere is catalyzed by processes that occur at Earth’s magnetopause (MP) in the electron diffusion region (EDR) of magnetic reconnection. Until the launch of the Magnetospheric Multiscale (MMS) mission, only rare, fortuitous circumstances permitted a glimpse of the electron dynamics that break magnetic field lines and energize plasma. 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. This has resulted in over 35 potential MP EDR encounters but is labor- and resource-intensive; after manual reclassification, just $\sim$ 0.7\% of MP crossings, or ~0.0001\% of the mission lifetime during MMS’s first two years contained an EDR. We introduce a Long-Short Term Memory (LSTM) Recurrent Neural Network (RNN) to detect MP crossings and automate the SITL classification process. The LSTM has been implemented in the MMS data stream to provide automated predictions to the SITL. This model facilitates EDR studies and helps free-up mission operation costs by consolidating manual classification processes into automated routines.
Keywords: Scientist in the Loop (SITL), Burst Data Management, Magnetopause, Ground loop, Magnetospheric Multiscale (MMS), Long short-term memory (LSTM), supervised learning, Event classification, Statistical studies
Received: 31 Mar 2020;
Accepted: 16 Jul 2020.
Copyright: © 2020 Argall, Small, Piatt, Breen, Petrik, Barnum, Kokkonen, Larsen, Wilder, Oka, Torbert, Ergun, Phan, Giles and Burch. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: PhD. Matthew R. Argall, University of New Hampshire, Durham, United States, email@example.com