Abstract
A binary complex plasma consists of two different types of dust particles in an ionized gas. Due to the spinodal decomposition and force imbalance, particles of different masses and diameters are typically phase separated, resulting in an interface. Both external excitation and internal instability may cause the interface to move with time. Support vector machine (SVM) is a supervised machine learning method that can be very effective for multi-class classification. We applied an SVM classification method based on image brightness to locate the interface in a binary complex plasma. Taking the scaled mean and variance as features, three areas, namely small particles, big particles and plasma without dust particles, were distinguished, leading to the identification of the interface between small and big particles.
Highlights
Institut für Materialphysik im Weltraum, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Magnetic Confinement Fusion Research Centre, Ministry of Education, Shanghai 201620, China
We applied an Support vector machine (SVM) classification method based on image brightness to locate the interface in a binary complex plasma
We applied the SVM method to achieve automatic identification of the interface in a binary complex plasma based on the brightness of the recorded images
Summary
Institut für Materialphysik im Weltraum, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Magnetic Confinement Fusion Research Centre, Ministry of Education, Shanghai 201620, China. We applied an SVM classification method based on image brightness to locate the interface in a binary complex plasma. We applied the SVM method to achieve automatic identification of the interface in a binary complex plasma based on the brightness of the recorded images. The cloud of small particles, big particles, and microparticle-free plasma can be distinguished with the naked eye.
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