Linear particle accelerators are complex and expensive to operate. Understanding the beam physics interactions within these accelerators is crucial for improving performance and reducing costs. Traditional physics simulations, while effective, are computationally demanding. Our novel approach addresses this challenge by replacing intensive computations with a machine learning model. This model predicts outcomes in milliseconds, significantly faster than conventional simulations. This breakthrough enables real-time physics predictions, facilitating online models that can predict diagnostics without harming the beam.
This project introduces an innovative simulation infrastructure for the SLAC FACET-II group. Our goal is to enhance existing physics simulations using advanced algorithms. The new system captures data at each step of the optimization process, refining input parameters for better outcomes. We then utilized this simulation data to develop a machine learning model.
The core of our model is a simple feedforward neural network. This network successfully predicts key parameters like beam emittance and bunch length based on various inputs. The accuracy of our model demonstrates its potential in revolutionizing the way we operate and understand linear particle accelerators.
The integration of machine learning into accelerator physics marks a significant advancement. It promises more efficient operations, reduced costs, and less invasive diagnostic techniques. This project paves the way for smarter, more responsive accelerator systems, opening new possibilities in particle physics research.
For more information or inquiries, please contact [sanjeev.chauhan@duke.edu].