  Global-scale energy flow throughout Earth's magnetosphere (MSP) 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 MSP, 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. We introduce a
Long-Short Term Memory (LSTM) Recurrent Neural Network model to detect MP
crossings and assist studies of energy transfer into the MSP. 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 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; 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.
