Accurate Imaging of Particle Motion in Dusty Plasmas Público

Cho, Jonathan (Spring 2022)

Permanent URL: https://etd.library.emory.edu/concern/etds/m900nv74g?locale=es
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Abstract

The use of cameras to track the trajectory of objects is important in the science of imaging. Therefore, accuracy in locating particle motion is of high priority. We explore the motions of dusty plasma in a weakly-ionized Argon-filled vacuum chamber in real experiments using a Phantom V7.11 high-speed camera. Then we simulate dusty plasma motion and track its trajectory using Trackpy, a Python tracking package. An issue called pixel locking arises when tracking few-pixel wide objects, which make particle tracking inaccurate. Pixel locking is apparent in dusty plasma experiments where the sizes of particles are 11 um. The analysis of the characteristics of dusty plasmas in a controlled environment is heavily biased due to pixel locking. There is a commonly used method called the SPIFF method which mathematically finds the true location of particles given an estimated location fed by a tracking algorithm. However, we find that the SPIFF algorithm does not correctly solve the fundamental issues caused by pixel locking as huge amounts of error are found when comparing the velocity probability density functions of a simulated particle trajectory and a SPIFF corrected particle trajectory. 

Table of Contents

1 Introduction 1

1.1 Dusty Plasma . . . . . . . . . . . . . . . . . . . . . . . . . . . ..2

1.2 SPIFF Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3 Tracking Particles . . . . . . . . . . . . . . . . . . . . . . . .4

2 Methods 5

2.1 Plasma Experiment . . . . . . . . . . . . . . . . . . . . . . 5

2.2 Simple Harmonic Motion . . . . . . . . . . . . . . . . .7

2.3 Particle Trajectory Model . . . . . . . . . . . . . . . . 8

2.4 Particle Generation . . . . . . . . . . . . . . . . . . . . . .11

2.5 Error Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . ..13

2.6 Velocity Distribution . . . . . . . . . . . . . . . . . . . . .15

3 Results and Discussion 17

4 Conclusion 20

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