A machine learning approach to shaping magnetic fringe fields for beam dynamics control

Abstract

Fringe fields at the entrances and exits of multipole magnets can adversely affect the dynamics of particles in an accelerator, but there is also the possibility that fringe fields could be used to enhance accelerator performance. Design work could benefit from computational tools for constructing realistic models of multipole fringe fields at an early stage in the design process; and methods for relating the magnet geometry to the field shape and to the beam dynamics would also be of significant value. We explore novel techniques to produce magnet designs that satisfy specific requirements for the beam dynamics. Machine learning tools are used to link properties of the beam dynamics in the fringe fields to the magnet geometry in an efficient way.