In an ongoing game of cosmic hide and seek, scientists have a new tool that may give them an edge. Physicists at the U.S. Department of Energy's Princeton Plasma Physics Laboratory have developed a computer program incorporating machine learning that could help identify blobs of plasma in outer space known as plasmoids. In a novel twist, the program has been trained using simulated data.
"As far as we know, this is the first time that anyone has used artificial intelligence trained on simulated data to look for plasmoids," said Kendra Bergstedt, a graduate student in the Princeton Program in Plasma Physics, which is based at PPPL. Bergstedt was the first author of the paper reporting the results in Earth and Space Science. The work pairs the Lab's growing expertise in computational sciences with its long history of exploring magnetic reconnection.
The use of machine learning will only become more common in astrophysics research, according to the scientists."It could particularly be helpful when making extrapolations from small numbers of measurements, as we sometimes do when studying reconnection," said Ji."And the best way to learn how to use a new tool is to actually use it. We don't want to stand on the sidelines and miss an opportunity.
As Bergstedt and Ji improve the plasmoid-detecting program, they hope to take two significant steps. The first is performing a procedure known as domain adaptation, which will help the program analyze datasets that it has never encountered before. The second step involves using the program to analyze data from the MMS spacecraft."The methodology we demonstrated is mostly a proof of concept since we haven't aggressively optimized it," Bergstedt said.
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