Scientists have developed a computer program that uses machine learning to help identify mysterious blobs of plasma, called plasmoids, in the Earth’s magnetosphere. This breakthrough could lead to a better understanding of magnetic reconnection, a process that can disrupt communications and the electrical grid.
Decoding the Cosmos with AI
A fourth state of matter in the universe: in this vast space of the world, scientists have long been busy. One goal in particular? Those are plasmoids, or blobs of plasma, that meander in the Earth’s magnetosphere—its magnetic shield—in which they live.
Click to read ‘Scientists develop a computer algorithm that helps spacecraft set ISEE magnetic traps’Doing so is like playing cosmic hide and seek but now researchers at the Princeton Plasma Physics Laboratory (PPPL) have created a new tool in this game. They have built the computer program through machine learning, to chug through staggering amounts of data collected by spacecraft and recognize plasmoids in all their hidden face. Moreover, the program is trained with simulated data so as to better learn to identify many different types of plasma signatures even in cases where they do not correspond well to idealized mathematical models.
This has been the subject of attempts to understand — The Mystery of Magnetic Reconnection
Actually, their end game is to… By studying magnetic reconnection, a process that takes place not only in Earth’s magnetosphere, but also throughout the universe. Magnetic reconnection occurs when magnetic field lines in a plasma converge, break apart violently, reconnect and release large amounts of energy. This interference can wreak havoc on things like communications satellites, cell phones, and even knock out the power grid.
The effect of plasmoids on the nesting sites that result, however, is less clear; scientists hypothesize that these collections may influence the speed with which reconnection occurs and/or how much energy it imparts to its environment. If scientists want to really comprehend this relationship, then they must also find a way to consistently see these ghostly blobs of plasma and keep them in sight. This is where the new AI-empowered program comes in place.
To our knowledge, this is the first use of AI trained on simulated data for the purpose of plasmoid hunting,’ said Kendra Bergstedt, a graduate student at PPPL and lead author of the paper detailing the research. “Making sure we are measuring plasmoids more effectively would aid us in understanding the long-time puzzle about how magnetic reconnection operates,” Cassak said.
Conclusion
The creation of this new AI program allows astrophysicists to do just that and represents a giant leap in our knowledge of the intricate processes which govern our cosmic world. Now, through applications of machine learning, scientists can begin to sort through broad swathes of data that may reveal the unassuming thread underlying magnetic reconnection processes. While the researchers continue to further develop and fine-tune their tool, they are excited by the potential for future observations not only to shed light on our very basic understanding of our place in the Universe, but also help us better predict where we may be vulnerable here on Earth to impacts from such energetic events given today’s technology-driven society.
