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

TITLE=MMS SITL Ground Loop: Automating the Burst Data Selection Process

JOURNAL=Frontiers in Astronomy and Space Sciences

VOLUME=Volume 7 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/astronomy-and-space-sciences/articles/10.3389/fspas.2020.00054

DOI=10.3389/fspas.2020.00054

ISSN=2296-987X

ABSTRACT=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.