Georges: A modular Python library for seamless beam dynamics simulations and optimization

Abstract

Particle tracking codes such as MAD-X or TRANSPORT commonly use a matrix formalism to propagate beams through magnetic elements as it simplifies the analysis of particle behavior, facilitates beam optimization and component design, and enables accurate particle accelerator simulations. However, these codes are inefficient when tracking many particles or accounting for energy degradation along the beamline. To overcome these limitations, we introduce Georges, a Python library used in the field of particle accelerators for medical applications comprising two modules: Manzoni and Fermi. Manzoni is an efficient particle tracking code that can track many particles while calculating beam losses and energy degradation using the Fermi–Eyges formalism implemented in the Fermi module. In this paper, we present the implementation details of Georges, which includes a verification conducted against other software tools such as MAD-X and BDSIM, along with a documentation on computational time.

Keywords

• Particle tracking
• Optimization
• Energy degradation
• Python