Machine learning could aid efforts to answer long-standing astrophysical questions

Machine learning could aid efforts to answer long-standing astrophysical questions


Newswise — In the ongoing game of cosmic hide-and-seek, scientists have a new tool that could give them the edge. physicist in usa Department of Energy(DOE) Princeton Plasma Physics Laboratory (PPPL) has developed a computer program machine learning which can help identify droplets Plasma In outer space are known as plasmoids. In a new twist, the program has been trained using simulated data.

The program will sift through troves of data collected by spacecraft on the magnetosphere, the region of outer space strongly influenced by Earth’s magnetic field, and the telltale signs of elusive blobs. Using this technique, scientists hope to learn more about the processes that control magnetic reconnection, a process that occurs in the magnetosphere and throughout the universe that can damage communications satellites and electrical grids.

Scientists believe so machine learning Plasmoid-detection could improve efficiency, aid in the basic understanding of magnetic reconnection, and allow researchers to better prepare for the consequences of reconnection-generated disturbances.

“As far as we know, this is the first time that anyone has used artificial intelligence “trained on simulated data to see the plasmoids,” said Kendra Bergstadis a graduate student in Princeton Program in Plasma Physics, which is based on PPPL. Bergstedt was the first author of paper Reporting of results in Earth and space sciences. This work combines the lab’s growing expertise in computational science with its long history of exploring magnetic reconnection.

looking for a link

Scientists want to find reliable, accurate ways to detect plasmoids so they can determine whether they affect magnetic reconnection, a process in which magnetic field lines separate, violently reconnect and Release huge amounts of energy. When it nears Earth, reconnection can trigger a cascade of charged particles falling into the atmosphere, disrupting satellites, mobile phones and electrical grids. “Some researchers believe that plasmoids aid in rapid recombination into larger plasmas,” said Mr Hantao, Professor of Astrophysics at Princeton University and a Distinguished Research Fellow at PPPL. “But those hypotheses have not yet been proven.”

Researchers want to know whether plasmoids can change the rate at which recombination occurs. They also want to know how much energy recombination provides to the plasma particles. “But to clarify the connection between plasmoids and recombination, we need to know where the plasmoids are,” Bergstedt said. “This is it machine learning “Can help us do that.”

The scientists used computer-generated training data to ensure that the program could recognize a range of plasma signatures. Typically, plasmoids created by computer models are idealized versions based on mathematical formulas – such as perfect circles – that do not often occur in nature. If the program was trained to recognize only these correct versions, it might miss versions of other shapes. To prevent those omissions, Bergstedt and Gee decided to use artificial, intentionally incomplete data so that the program would have an accurate baseline for future studies. “Compared with mathematical models, the real world is a mess,” Bergstedt said. “So we decided to let our program learn using fluctuating data that you would find in real observations. For example, instead of starting our simulation with a perfectly flat electric current sheet, we give our sheet some undulations. We are hoping that machine learning The approach may allow for greater nuance than a strict mathematical model.” This research continues previous attempts In which Bergstedt and Gee wrote computer programs that included more idealized models of plasmoids.

Using the machine learning According to scientists, this will become more common in astrophysics research. “This can be especially helpful when extrapolating from small numbers of measurements, as we sometimes do when studying reconnection,” Gee said. “And the best way to learn how to use a new tool is to actually use it. We don’t want to miss any opportunities by standing on the sidelines.”

Bergstedt and Ji plan to use the plasmoid-detecting 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 through plasma in the magnetotail, the region in space that points away from the Sun that is controlled by Earth’s magnetic field. Is.

The magnetotail is an ideal place to study reconnection because it combines accessibility with scale. “If we study reconnection by looking at the Sun, we can only take measurements from a distance,” Bergstedt said. “If we look at reconnection in the laboratory, we can put our instruments directly into the plasma, but the size of the plasma will typically be smaller than what is found in space.” The study of reconnection in magnetotail is an ideal middle option. “This is a large and naturally occurring plasma that we can measure directly using a spacecraft flying through it,” Bergstedt said.

As Bergstedt and Ji improve the plasmoid-detecting program, they hope to take two important steps. The first is performing a process known as domain adaptation, which will help the program analyze datasets it has never encountered before. The second phase involves using programs to analyze data from the MMS spacecraft. “The functionality we’ve demonstrated is mostly a proof of concept because we haven’t aggressively optimized it,” Bergstedt said. “We want the model to work even better than it does now, start applying it to real data and then we’ll go from there!”

This research was supported by DOE fusion energy science program under contract DE-AC0209CH11466, by NASA under grants NNH15AB29I and 80HQTR21T0105, and by the National Science Foundation Graduate Research Fellowship under grant DGE-2039656.

PPPL is mastering the art of using plasma – the fourth state of matter – to solve some of the world’s toughest science and technology challenges. Located on Princeton University’s Forrestal Campus in Plainsboro, New Jersey, our research ignites innovation in many 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 on https://energy.gov/science And http://www.pppl.gov,

(TagstoTranslate)Newswise(T)Machine Learning;Artificial Intelligence;Plasma;Plasma Physics and Controlled Fusion;Plasma Astrophysics;Magnetosphere;Satellites;Reconnection;Magnetic Reconnection(T)All Journal News(T)Artificial Intelligence(T)Physics(T ) Space & Astronomy (T) DOE Science News Sources (T) Embargoed Feeds – Hidden (T) Top Hit Stories (T) Princeton Plasma Physics Laboratory