Researchers from the Department of Energy’s SLAC National Accelerator Laboratory, Argonne National Laboratory and the University of Chicago have developed an algorithm that can more accurately predict the shape of particle beams travelling through accelerators. This breakthrough algorithm, which pairs machine-learning techniques with classical beam physics equations, can create a detailed picture of the beam with much less data-crunching than traditional methods.
Whenever an accelerator is operational, billions of electrons are packed together and travel at nearly the speed of light through metal piping. These particular electron bunches, known as particle beams, are used to gain insights into the behavior of molecules, materials and other elements. However, it is difficult to estimate what a particle beam looks like in a single snapshot as it travels throughout an accelerator.
Fortunately, the recent algorithm developed by the research team can enable scientists to generate highly accurate profiles of the beam more reliably. By utilizing knowledge of particle beam dynamics and interpreting experimental data from the Argonne Wakefield Accelerator, the algorithm was able to produce three-dimensional diagrams of the beam using just 10 data points – a feat that could have taken thousands using more traditional methods.
The algorithm represents a substantial shift in the manner of analyzing experimental data from accelerators. Not only does it save a great deal of time and effort, it also offers researchers a better insight into the shape of the particle beam. With further development and refinement, the algorithm could also be used to reconstruct the full 6D phase-space of the beam, adding to its functionality and accuracy.
SLAC National Accelerator Laboratory is a multi-program laboratory operated by Stanford University for the U.S. Department of Energy Office of Basic Energy Sciences and is located in Menlo Park, California. It is the longest operating accelerator in the world and was founded in 1962.
In this current venture, the lead co-author is Ryan Roussel, SLAC accelerator scientist. He is a physicist who specializes in the detailed study of particle beams in accelerators, specifically focusing on the challenges related to measurement, processing, and data analysis at information-rich accelerators. His research also covers the related areas of machine learning, particle-in-cell simulations and controllability.
