Linac_Gen: integrating machine learning and particle-in-cell methods for enhanced beam dynamics at Fermilab

TL;DR

Linac_Gen is a novel tool at Fermilab that integrates machine learning with Particle-in-Cell methods to significantly improve the speed and accuracy of beam dynamics simulations in linear accelerators.

Contribution

It introduces Linac_Gen, combining ML algorithms with PIC methods, notably using genetic algorithms for rapid phase-space matching and enhanced 3D field map handling.

Findings

Tenfold increase in phase-space matching speed

Improved simulation accuracy for beam instabilities

Enhanced efficiency in linac system design

Abstract

Here, we introduce Linac_Gen, a tool developed at Fermilab, which combines machine learning algorithms with Particle-in-Cell methods to advance beam dynamics in linacs. Linac_Gen employs techniques such as Random Forest, Genetic Algorithms, Support Vector Machines, and Neural Networks, achieving a tenfold increase in speed for phase-space matching in linacs over traditional methods through the use of genetic algorithms. Crucially, Linac_Gen's adept handling of 3D field maps elevates the precision and realism in simulating beam instabilities and resonances, marking a key advancement in the field. Benchmarked against established codes, Linac_Gen demonstrates not only improved efficiency and precision in beam dynamics studies but also in the design and optimization of linac systems, as evidenced in its application to Fermilab's PIP-II linac project. This work represents a notable advancement in accelerator physics, marrying ML with PIC methods to set new standards for efficiency and accuracy in accelerator design and research. Linac_Gen exemplifies a novel approach in accelerator technology, offering substantial improvements in both theoretical and practical aspects of beam dynamics.