MMS SITL Ground Loop: Automating the Burst Data Selection Process

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.

Keywords: burst data management; ground loop; long-short term memory (LSTM); magnetopause; magnetospheric multiscale (MMS); mission operations; scientist in the loop (SITL).

FIGURE 1 |

The GLS magnetopause model is an example of a supervised learning model that made use of data labeled previously by the SITL for training and testing. Applying historical SITL labels to preprocessed data significantly reduce the time needed for model development.

FIGURE 2 |

Evolution of the non-linearity and time-dependence of neural network models that make up the bidirectional Long Short Term Memory GLS magnetopause model.

FIGURE 3 |

The Architecture of the GLS magnetopause model mimicks how scientists interpret data. It makes decisions by placing data in the context of past and future observations.

FIGURE 4 |

Burst selections made by three different SITL scientists surrounding the magnetopause crossings of three separate orbits. Just as the SITLs have different opinions on how and what to select, and with what FOM value, so too do the GLS and ABS.

FIGURE 5 |

A comparison of SITL, GLS, and ABS segments from SROI1 that takes into account the range-based nature of event selection intrinsic to time series data. (A) Considers all SITL selections whereas (B) includes only SITL-classified magnetopause selections.

FIGURE 6 |

Point-by-point comparison between (A) all SITL and GLS selections, (B) SITL selections filtered for magnetopause crossings and GLS selections, and (C) all SITL and ABS selections. Such a point-wise metric is typical for machine learning models but does not properly weight predictions with partial or multiple overlap, as when the SITL selects additional context around a given event (Figure 4, Orbit 1055), or when multiple GLS selections are encompassed by one SITL selection (Figure 4, Orbit 1058).

FIGURE 7 |

Trade-off between over-selecting false events and under-selecting true events. For MMS, electron diffusion regions are rare and difficult to impossible to observe with the SITL data, so the SITL is willing to over-select by choosing all MP crossings, but they still has to contend with telemetry restrictions. The choice of thresholds for the GLS MP model tries to emulate this approach.

FIGURE 8 |

A hierarchy of machine learning models to automate science objectives and reduce mission costs. Data is filtered through region classifiers (green rectangles) and passed to specialized event classifiers (black text) specific to those regions. Multiple event classifiers are activated together to automate science campaigns (blue ovals).