Date of Award

Spring 1-1-2018

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

First Advisor

Zoltan Sternovsky

Second Advisor

Robert Marshall

Third Advisor

Xinlin Li

Fourth Advisor

Scot Elkington

Fifth Advisor

Craig Pollock

Abstract

This work describes technical innovations to improve the data quality and volume for the Fast Plasma Investigation (FPI) on board the Magnetospheric Multiscale mission (MMS). A parametric study of wavelet compression has shown that plasma count data can be compressed to high compression ratios with a minimal effect on the integrated plasma moments. Different regions of the magnetosphere are analyzed for both electron and ion count data. The FPI trigger data, intended as a data ranking metric, has been adapted and corrected to a point where scientifically accurate pseudo moments can be generated and released to the research community, drastically increasing the availability of high time resolution data. This is possible due to a scaling system that tunes the dynamic range of the system per region, and the method of using a neural network to correct for exterior contamination effects, such as spacecraft potential. Finally, a map of detection angle bias has been generated that can be used to correct raw count for errors in look direction of incoming particles. This map was generated by statistically sampling particle flight paths through a charged spacecraft environment, validating against flight data. All three of these efforts lead toward the overarching goal of improving data quality and volume for the FPI suite, and future missions to come.

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