Dynamical Inference of Forces in Dusty Plasmas using Three-Dimensional Laser Sheet Tomography

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This award will enable novel studies of complex "dusty" plasmas using machine learning techniques on data from laboratory experiments. Some of the most pressing scientific and societal issues involve the dynamics of complex systems, such as Earth's variable climate, global financial markets, urban environments, and the migration of animals. These systems have many interacting components, yet it is unclear how they are entangled together to produce the resulting dynamics. In the emerging era of Big Data, these systems and their components can be measured exceedingly well, resulting in a vast amount of data. In the pursuit to provide a deeper understanding of such systems, dynamical inference has emerged as an important field in physics and computer science. However, many state-of-the-art computational techniques for teasing apart complex dynamics are tested on simulated data. This award will combine machine learning techniques from this emerging field with high-resolution data from laboratory experiments of dusty plasmas, which consist of levitated, micron-sized dust particles embedded in a plasma of electrons and ions. By simultaneously tracking the motion of hundreds of particles, the fundamental, nonequilibrium forces that drive their motion will be "learned" using Artificial Intelligence techniques, exploring the boundary between physical, human intuition and the limits of what physics can be uncovered through machine learning. In parallel to this research effort, the project includes continuation of an after-school science club at a local elementary school in Dekalb county, which hosts the 3rd largest school system and is the most diverse county in Georgia.The motion of dust particles in plasma has been studied for nearly 30 years and lead to the discovery of strongly coupled crystalline structures and nonequilibrium dynamics governed by gravitational, hydrodynamic, and electromagnetic forces. Many of the interparticle forces are complex; they can be non-reciprocal, and non-additive. Deciphering the mélange of potential interactions between many particles is a challenging task, and could open a new paradigm of understanding in dusty plasmas in extreme environments. This award aims to tease apart both the known and unknown forces that drive dusty plasmas using high-resolution, three-dimensional imaging and particle tracking coupled with modern dynamical inference techniques. This will effectively turn every dust particle into a local probe of the plasma environment and can be applied in a diverse array of experimental settings. To accomplish this, a new scanning laser-sheet tomography technique will be developed that utilizes a single high-speed camera. In systems of ~100 particles or less levitated above a biased electrode in an rf plasma, the motion of single particles will be tracked for 30 s or longer. A detailed analysis of their movement will reveal in-situ information about the plasma environment, including the plasma sheath, and the local stochastic fluctuations of charge on the particles. For highly dynamic systems of many particles, the large quantity of data acquired over long times will be analyzed using a variety of machine learning techniques. The team will compute quantities such as the effective pair potential between particles, and more complex, three-body interactions and non-reciprocal forces will be extracted using neural networks with varying hierarchies. More broadly, this award aims to test the limit of what a machine can "learn" from noisy, experimental data, and explore new physics from complex, big data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.