An innovative artificial intelligence algorithm has been designed to accurately predict the distribution of particles in a beam within a particle accelerator, demonstrating the ability to infer complex, high-dimensional beam structures from surprisingly minimal data.

Particle accelerators play a crucial role as some of the most significant (and largest) experimental instruments in contemporary physics. These devices propel particle beams through metallic tubes at near-light speed, allowing scientists to examine the atomic interactions of molecules and the tiniest subatomic particles. Understanding how a particle beam will react during a specific experiment is essential to optimize the valuable scientific data obtained. This becomes even more critical as accelerators function at increasingly higher energies and generate more intricate beam patterns. However, pinpointing particle behavior is a challenging endeavor, as particle beams often consist of billions of particles, making it difficult to determine the final position of each one.

Researchers from the US Department of Energy’s SLAC in California and the University of Chicago have now created a machine learning algorithm that provides a more accurate representation of the particle distribution in an accelerated beam. Ryan Roussel, an accelerator scientist at SLAC, says, “We have many ways to control particle beams inside accelerators, but we lack a truly accurate method for defining a beam’s form and momentum.” “Our algorithm utilizes information about a beam that is usually disregarded, using it to create a more comprehensive image of the beam.”

Traditionally, researchers employ a statistical approach to outline the velocity and location of particles, offering a general view of the beam’s overall form. However, this method may overlook valuable information. Alternatively, scientists can evaluate how a beam may appear under varying experimental conditions by gathering extensive measurements of the beam itself. While some of these methods already employ machine learning, they demand vast quantities of data and computational resources. In their latest research, the team devised a machine learning model that combines the most effective aspects of both techniques. Their algorithm utilizes our understanding of beam dynamics to predict the collective “phase space distribution” of particle velocities and positions.

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“Typical machine learning models do not directly incorporate particle beam dynamics to accelerate learning and minimize the data needed,” says a SLAC accelerator scientist. “We’ve demonstrated that we can deduce incredibly intricate high-dimensional beam structures from a remarkably small amount of data.” The Argonne Wakefield Accelerator at the DOE’s Argonne National Laboratory, which is located close to Chicago, Illinois, is where the researchers tested their model. They were able to effectively interpret experimental data using particle beam physics with just 10 data points, as opposed to a model developed using machine learning that had not been trained in particle beam dynamics needing up to 10,000 data points to do the same thing. The current model can reconstruct a particle beam in a 4D beam phase space, including the up-down and left-right axes. The team is now working on achieving a complete 6D phase space distribution that encompasses particle velocities along the beam’s direction.

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