Machine Learning approach gives insights into magnetic history: Research

Magnets used in accelerators work similarly. Their past experiences, or what passed through them like an electric current, have an impact on how they will behave in the future. Before beginning a new experiment, scientists may need to completely reset a magnet without knowing its history; this can take ten or fifteen minutes. With some accelerators having hundreds of magnets, the process can easily grow expensive and time-consuming.

According to research published in the journal 'Physical Review Letters', a group of scientists from the SLAC National Accelerator Laboratory of the Department of Energy and other organisations have created a potent mathematical method that makes use of ideas from machine learning to model the previous states of a magnet and forecast its future states. With this innovative method, there is no longer a need to reset the magnets, and the accelerator performance improves right away.

"Our technique fundamentally changes how we predict magnetic fields inside accelerators, which could improve the performance of accelerators across the world," SLAC associate scientist Ryan Roussel said. "If the history of a magnet isn't well-known, it will be difficult to make future control decisions to create the specific beam that you need for an experiment."

The team's model looks at an important property of magnets known as hysteresis, which can be thought of as residual, or leftover, magnetism. Hysteresis is like the leftover hot water in your shower pipes after you have turned the hot water off. Your shower will not immediately become cold - the hot water that is left in the pipes must flow out of the showerhead before only cold water is left.

"Hysteresis makes tuning magnets challenging," SLAC associate scientist Auralee Edelen said. "The same settings in a magnet that resulted in one beam size yesterday might result in a different beam size today due to the effect of hysteresis."

The team's new model removes the need to reset magnets as often and can enable both machine operators and automated tuning algorithms to quickly see their present state, making what was once invisible visible, Edelen said.

Ten years ago, many accelerators did not need to consider sensitivity to hysteresis errors, but with more precise facilities like SLAC's LCLS-II coming online, predicting residual magnetism is more critical than ever, Roussel said.

The hysteresis model could also help smaller accelerator facilities, which might not have as many researchers and engineers to reset magnets, run higher-precision experiments. The team hopes to implement the method across a full set of magnets at an accelerator facility and demonstrate an improvement in predictive accuracy on an operational accelerator.

This research was supported by the U.S. Department of Energy's Office of Basic Energy Sciences. The SLAC Metrology group and the Advanced Photon Source also supported this work. (ANI)