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Generalized Time-Series Analysis for In-Situ Spacecraft Observations: Anomaly Detection and Data Prioritization using Principal Components Analysis and Unsupervised Clustering
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  • Matthew G. Finley,
  • Miguel Martínez Ledesma,
  • William Paterson,
  • Matthew R Argall,
  • David Michael Miles,
  • John C. Dorelli,
  • Eftyhia Zesta
Matthew G. Finley
University of Maryland, College Park

Corresponding Author:[email protected]

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Miguel Martínez Ledesma
Catholic University of America
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William Paterson
NASA Goddard Spaceflight Center
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Matthew R Argall
University of New Hampshire
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David Michael Miles
University of Iowa
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John C. Dorelli
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Eftyhia Zesta
NASA Goddard
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In-situ spacecraft observations are critical to our study and understanding of the various phenomena that couple mass, momentum, and energy throughout near-Earth space and beyond. However, on-orbit telemetry constraints can severely limit the capability of spacecraft to transmit high-cadence data, and missions are often only able to telemeter a small percentage of their captured data at full rate. This presents a programmatic need to prioritize intervals with the highest probability of enabling the mission’s science goals. Larger missions such as the Magnetospheric Multiscale mission (MMS) aim to solve this problem with a Scientist-In-The-Loop (SITL), where a domain expert flags intervals of time with potentially interesting data for high-cadence data downlink and subsequent study. Although suitable for some missions, the SITL solution is not always feasible, especially for low-cost missions such as CubeSats and NanoSats. This manuscript presents a generalizable method for the detection of anomalous data points in spacecraft observations, enabling rapid data prioritization without substantial computational overhead or the need for additional infrastructure on the ground. Specifically, Principal Components Analysis and One-Class Support Vector Machines are used to generate an alternative representation of the data and provide an indication, for each point, of the data’s potential for scientific utility. The technique’s performance and generalizability is demonstrated through application to intervals of observations, including magnetic field data and plasma moments, from the CASSIOPE e-POP/Swarm-Echo and MMS missions.
20 Apr 2024Submitted to ESS Open Archive
26 Apr 2024Published in ESS Open Archive