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Algorithmic Detection and Statistica...

Bergstedt, Kendra A.,

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Algorithmic Detection and Statistical Analyses of Plasmoids in Multiple-X-Line Collisionless Magnetotail Reconnection /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Algorithmic Detection and Statistical Analyses of Plasmoids in Multiple-X-Line Collisionless Magnetotail Reconnection // Kendra A Bergstedt.
Author:	Bergstedt, Kendra A.,
Description:	1 electronic resource (197 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
Contained By:	Dissertations Abstracts International86-12B.
Subject:	Plasma physics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31839767
ISBN:	9798280749504

Algorithmic Detection and Statistical Analyses of Plasmoids in Multiple-X-Line Collisionless Magnetotail Reconnection /
Bergstedt, Kendra A.,

Algorithmic Detection and Statistical Analyses of Plasmoids in Multiple-X-Line Collisionless Magnetotail Reconnection /Kendra A Bergstedt. - 1 electronic resource (197 pages)

Source: Dissertations Abstracts International, Volume: 86-12, Section: B.

Correctly identifying structures during multiple-X-line reconnection is crucial for understanding the couplings of the microscale to the macroscale, such as the potential role that the plasmoid instability plays in reconnection dynamics. This is particularly relevant to studying reconnection regions via analyzing in-situ spacecraft data. One spacecraft traces a 1D path through the 3D plasma. This limitation makes detection of dynamic plasma structures difficult. As such, this work focuses on the developing of algorithms which detect plasmoids in in-situ magnetotail data, and the physics resulting from them.We first develop and use a hand-tuned algorithm. We use measurements from the Magnetospheric Multiscale (MMS) mission to perform the first statistical study of magnetic structures and associated energy dissipation observed during a single period of turbulent magnetic reconnection. The size of the plasmoids and other structures forms a decaying exponential distribution. The magnetic structures are locations of significant energy dissipation via parallel electric field, while dissipation via perpendicular electric field dominates outside of the structures. Significant energy also returns from particles to fields.We next present a method for creating spacecraft-like data which can be used to train Machine Learning (ML) models to detect plasmoids in in-situ magnetotail data. We develop a method for generating "messy" 2D simulation data from which simulated spacecraft trajectories can be constructed. This simulated data is used as training data for ML models intended for use on spacecraft data. The classifier we train is able to detect more than 70% of the plasmoids in the dataset but also has a high false positive rate.We next utilize domain adaptation techniques to adapt our classifier to MMS data. A data pipeline is constructed to process data from the magnetotail. Multiple domain adaptation methods are implemented to construct MMS feature representations that resemble the PIC feature representations that the classifier uses. These are then fed into the classifier to generate predictions. The resulting MMS classifiers are applied to an existing catalog of magnetotail plasmoids. Insights from the models' performance are presented and discussed.

English

ISBN: 9798280749504Subjects--Topical Terms:

3175417
Plasma physics.
Subjects--Index Terms:

Magnetotail plasmoids

Algorithmic Detection and Statistical Analyses of Plasmoids in Multiple-X-Line Collisionless Magnetotail Reconnection /
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