Including the physics of particle beam dynamics with the experimental data allowed the researchers to accurately reconstruct fine details of the beam using only 10 data points – a task that might take up to 10,000 data points for some machine learning models that don’t include a model of beam physics. To test their ideas, the team used their model to interpret experimental data from the Argonne Wakefield Accelerator at the DOE’s Argonne National Laboratory. ![]() Alternatively, beam scientists can take many measurements of the beam itself and try to reconstruct, sometimes using machine learning, what the beam would look like under different experimental circumstances – but those methods require a lot of data and a lot of computational power.įor this study, the team tried a new approach: They built a machine-learning model that uses our understanding of beam dynamics to predict the distribution of particles positions and speeds within the beam, collectively known as the beam’s phase space distribution. Typically, researchers describe the positions and speeds of particles in a beam in terms of a few summary statistics that provide a rough shape of the beam overall – but that approach throws out a lot of potentially useful information. “Our algorithm takes into account information about a beam that is normally discarded and uses that information to paint a more detailed picture of the beam.” “We have a lot of different ways to manipulate particle beams inside of accelerators, but we don’t have a really precise way to describe a beam’s shape and momentum,” SLAC accelerator scientist and lead co-author Ryan Roussel said. The researchers detailed their algorithm and method in April 2023 in the journal Physical Review Letters. ![]() This detailed beam information will help scientists perform their experiments more reliably – a need that is becoming increasingly important as accelerator facilities operate at higher and higher energies and generate more complex beam profiles. ![]() Now, researchers at the Department of Energy’s SLAC, the DOE’s Argonne National Laboratory, and the University of Chicago have developed an algorithm that more precisely predicts a beam’s distribution of particle positions and velocities as it zips through an accelerator.
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