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

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|October 29, 2021
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Summary
This summary is machine-generated.

A new machine learning model automatically identifies magnetopause crossings, improving solar wind entry studies. This Long-Short Term Memory (LSTM) network assists the Magnetospheric Multiscale (MMS) mission by automating data analysis for energy transfer research.

Keywords:
burst data managementground looplong-short term memory (LSTM)magnetopausemagnetospheric multiscale (MMS)mission operationsscientist in the loop (SITL)

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Area of Science:

  • Space Physics
  • Plasma Physics
  • Machine Learning Applications

Background:

  • Earth's magnetopause (MP) is crucial for global energy flow and solar wind entry into the magnetosphere.
  • Magnetic reconnection at the MP drives magnetospheric dynamics, necessitating accurate MP identification for statistical studies.
  • Current methods for MP identification are manual, consuming significant research time.

Purpose of the Study:

  • To develop and implement an automated machine learning model for detecting magnetopause crossings.
  • To assist the Magnetospheric Multiscale (MMS) mission in identifying intervals for studying energy transfer and reconnection.
  • To reduce the manual effort required for event searches and data classification.

Main Methods:

  • A Long-Short Term Memory (LSTM) Recurrent Neural Network was developed to detect MP crossings.
  • The LSTM model was integrated into the operational data stream of the MMS mission.
  • Model performance was evaluated against Scientist-in-the-Loop (SITL) classifications of MP crossings.

Main Results:

  • The LSTM model successfully predicted 76% of SITL-classified MP crossings and 71% of its predictions were selected by the SITL.
  • Most non-classified predictions exhibited MP-like characteristics with mixed plasma environments.
  • The model demonstrated high accuracy in identifying MP crossings, aiding MMS mission operations.

Conclusions:

  • The developed LSTM model effectively automates magnetopause crossing detection, significantly aiding space physics research.
  • This automation frees up valuable researcher time and mission resources for in-depth analysis.
  • The public availability of the LSTM model and its predictions facilitates broader research into magnetospheric energy transfer and reconnection events.