Researchers at SLAC National Accelerator Laboratory, Argonne National Laboratory, and the University of Chicago have developed a new algorithm that enhances the prediction of particle beam distributions in accelerators, using a blend of classical physics equations and machine learning. As electrons move through the accelerator’s pipes at nearly the speed of light, understanding their precise behavior is crucial for experiments aimed at examining atomic and material properties. Traditionally, particle positions and velocities were estimated using summary statistics or extensive data, but the new method improves accuracy while dramatically reducing data processing requirements.
The innovative algorithm takes previously discarded beam information into account, reconstructing the beam’s phase space distribution using just 10 data points, a remarkable reduction compared to the 10,000 required by some existing models. This 4D beam phase space reconstruction represents a significant advancement in beam modeling. Researchers aim to extend this capability to 6D phase space distributions, handling more complex beam profiles as accelerator facilities evolve. Overall, the algorithm offers a comprehensive approach to utilizing accelerator data, promising to enhance scientific investigations across various applications. The findings were published in April 2023 in Physical Review Letters.
