Knowing a magnet’s past will allow scientists to customize particle beams more precisely in the future. As accelerators stretch for higher levels of performance, understanding subtle effects, such as those introduced by magnetic history, is becoming more critical.
After a long day of work, you might feel tired or exhilarated. Either way, you are affected by what happened to you in the past.
Accelerator magnets are no different. What they went through – or what went through them, like an electric current – affects how they will perform in the future.
Without understanding a magnet’s past, researchers might need to fully reset them before starting a new experiment, a process that can take 10 or 15 minutes. Some accelerators have hundreds of magnets, and the process can quickly become time-consuming and costly.
Now a team of researchers from the Department of Energy’s SLAC National Accelerator Laboratory and other institutions have developed a powerful mathematical technique that uses concepts from machine learning to model a magnet’s previous states and make predictions about future states. This new approach eliminates the need to reset the magnets and results in improvements in accelerator performance immediately.









