Machine learning could help identify plasmoids in space

Machine Learning


As part of their ongoing efforts to unlock the mysteries of the universe, scientists at the U.S. Department of Energy's Princeton Plasma Physics Laboratory (PPPL) have developed an innovative computer program that leverages machine learning and may help identify clumps of plasma in space, commonly known as plasmoids. What's unique about this program is that it was trained on simulated data.

The program is designed to analyze vast amounts of spacecraft data collected in the magnetosphere, the part of outer space strongly influenced by Earth's magnetic field, and highlight telltale indicators of these hard-to-detect clumps.

Scientists hope to use the technique to better understand magnetic reconnection, which can cause disruptions to communications satellites and power grids in the magnetosphere and beyond.

Scientists believe that using machine learning could improve their ability to pinpoint plasmoid locations, leading to a better understanding of magnetic reconnection and helping them prepare for the disruptions it causes.

“To our knowledge, this is the first time that artificial intelligence trained on simulated data has been used to look for plasmoids.” “We're excited to be working with the PPPL-based Princeton Plasma Physics Program,” said Kendra Bergstedt (Link is external) , a graduate student in the Princeton Plasma Physics Program based at PPPL.

Researchers are seeking reliable and accurate techniques to detect plasmoids to determine their impact on magnetic reconnection, which involves the violent separation and reconnection of magnetic field lines and the release of huge amounts of energy. When this process occurs close to Earth, it can trigger a stream of charged particles that enter the atmosphere, disrupting satellites, cellphones, and power grids.

“Some researchers believe that plasmoids help large plasmas reconnect quickly.” “This is a very exciting time for the physicists,” said Hantao Ji, a professor of astrophysics at Princeton University and distinguished research fellow at PPPL. “But those hypotheses remain unproven.”

The researchers are investigating whether plasmoids affect the speed of recombination and the amount of energy transferred to plasma particles. They are using computer-generated training data to make sure the program can identify different plasma features. Typically, computer-generated plasmoids are based on idealized mathematical formulas and often have shapes such as perfect circles that are not often seen in nature. To ensure that the program does not miss plasmoids with different shapes, the scientists intentionally used artificially incomplete data to provide an accurate baseline for future studies.

“Compared to mathematical models, the real world is complicated.” Bergstedt said. “So we decided to train the program using fluctuating data from real observations. For example, rather than starting with a perfectly flat current sheet, we gave the sheet some wobble. We hope that this machine learning approach will allow us to capture finer nuances than a strict mathematical model can.”

Bergstedt and Ji aim to use the plasmoid detection software to analyze data collected during NASA's Magnetospheric Multiscale (MMS) mission. Launched in 2015 to study reconnection, MMS consists of four spacecraft working together through the plasma of the magnetotail, the region of space that moves away from the Sun and is influenced by Earth's magnetic field.

The magnetotail provides an ideal environment for reconnection studies due to both its accessibility and size. “To observe recombination in the laboratory, we can put instruments directly into the plasma, but the size of the plasma is smaller than what is typically found in space.” Studying magnetotail reconnection is an ideal intermediate option. “This is a huge, naturally occurring plasma that we can measure directly using a spacecraft passing through it.” Bergstedt said.

Bergstedt and Ji are looking to make two key advancements in enhancing the plasmoid detection software. The first goal is to implement domain adaptation so the program can analyze unknown datasets. The second goal is to leverage the program to analyze data captured by the MMS spacecraft.

“The methodology we've demonstrated is largely a proof-of-concept as it hasn't been actively optimized.” Bergstedt said. “We want to make the model work even better than it does now, apply it to real data, and go from there.”

Journal References:

  1. K. Bergstedt, H. Ji. A new method to train classification models for structure detection in in situ spacecraft data. Earth and Space Sciences, 2024; DOI: 10.1029/2023EA002965





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