Scientists now have a new tool that may give them an edge in the ongoing game of hide-and-seek in space: physicists at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) have developed a computer program incorporating machine learning that could help identify clumps of plasma called plasmoids in space. What's novel is that the program was trained using simulated data.
The program will sift through reams of data collected by spacecraft within the magnetosphere — a region of space strongly influenced by Earth's magnetic field — to point out telltale signs of the elusive clump. Using the technique, scientists hope to learn more about the processes that govern magnetic reconnection, a process that occurs within the magnetosphere and throughout space and can cause damage to communications satellites and power grids.
Scientists believe that machine learning can improve our ability to spot plasmoids, advance our fundamental understanding of magnetic reconnection, and help researchers better prepare for the aftermath of disturbances caused by reconnection.
“To our knowledge, this is the first time we've used artificial intelligence trained on simulated data to look for plasmoids,” said Kendra Bergstedt, a graduate student in the Princeton Plasma Physics Program based at PPPL. Bergstedt is first author of a paper reporting the results in the journal Earth and Space Sciences. The research combines the lab's growing expertise in computational science with a long history of studying magnetic reconnection.
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Scientists want to find a reliable and accurate way to detect plasmoids so they can determine whether they affect magnetic reconnection, a process in which magnetic field lines separate and violently reconnect, releasing enormous amounts of energy. If the reconnection occurs near Earth, it could set off a chain reaction of charged particles that fall into the atmosphere, disrupting satellites, cellphones, and power grids. “Some researchers believe that plasmoids could facilitate fast reconnection in large-scale plasmas,” says Hantao Ji, a professor of astrophysics at Princeton University and a distinguished research associate at PPPL. “But these hypotheses remain to be proven.”
The researchers want to know if plasmoids can change the rate at which recombination occurs, and also measure how much energy recombination imparts to plasma particles. “But to figure out the relationship between plasmoids and recombination, we need to know where the plasmoids are,” Bergstedt says. “Machine learning can help us do that.”
The scientists used computer-generated training data to help the program recognize different plasma features. Typically, plasmoids created by computer models are idealized versions based on mathematical formulas with shapes that are not often seen in nature (such as perfect circles). If the program was only trained to recognize these perfect versions, it might miss ones with other shapes. To prevent these oversights, Bergstedt and Ji decided to use artificial, intentionally incomplete data so that the program would have an accurate baseline for future studies. “Compared to mathematical models, the real world is complicated,” says Bergstedt. “So we decided to train the program with data with fluctuations that come from real observations. For example, instead of starting the simulation with a perfectly flat current sheet, we give the sheet some wiggle. We hope that the machine learning approach will allow us to take into account finer nuances than a strict mathematical model could.” The study builds on Bergstedt and Ji's previous attempts to create a computer program that incorporated a more idealized model of plasmoids.
Scientists say the use of machine learning will become increasingly common in astrophysics research. “It can be especially useful when making inferences from a small number of measurements, as we sometimes do when studying recombination,” Ji said. “The best way to learn how to use a new tool is to actually use it. You don't want to sit on the sidelines and miss out.”
Bergstedt and Ji will use the Plasmoid Detection Program to examine data being collected by NASA's Magnetospheric Multiscale (MMS) mission. Launched in 2015 to study reconnection, MMS consists of four spacecraft flying in formation through the plasma of the magnetotail, a region of space away from the Sun that is controlled by Earth's magnetic field.
The magnetotail is an ideal place to study reconnection because it combines accessibility with scale. “If we were to study reconnection by observing the Sun, we could only measure from far away,” says Bergsted. “If we were to observe reconnection in the lab, we could put instruments directly into the plasma, but the size of the plasma is smaller than what we typically see in space.” Studying reconnection in the magnetotail is an ideal middle ground. “It's a large, naturally occurring plasma, and we can measure it directly using spacecraft passing through there,” says Bergsted.
As Bergsted and Ji improve their plasmoid-detection program, they hope to take two key steps. The first is to perform a procedure called domain adaptation, which will help the program analyze data sets it has never encountered before. The second step is to use the program to analyze data from the MMS spacecraft. “The methodology we've demonstrated is mostly a proof-of-concept because we haven't actively optimized it,” Bergsted says. “We'd like to make the model work even better than it does now, apply it to real data, and go from there.”
This research was supported by DOE's Fusion Energy Sciences Program under Contract No. DE-AC0209CH11466, NASA under Grant Nos. NNH15AB29I and 80HQTR21T0105, and a National Science Foundation Graduate Research Fellowship under Grant No. DGE-2039656.
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PPPL is mastering techniques using plasma, the fourth state of matter, to solve some of the world's toughest scientific and engineering challenges. Located on Princeton University's Forrestal Campus in Plainsboro, New Jersey, our research is inspiring innovation in a variety of applications, including fusion energy, nanoscale manufacturing, quantum materials and devices, and sustainability science. The university manages the laboratory for the U.S. Department of Energy's Office of Science, the nation's largest supporter of basic research in the physical sciences. Feel the heat at https://energy.gov/science and http://www.pppl.gov.
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