Machine learning based phase space tomography using kicked beam turn-by-turn centroid data in a storage ring
Abstract
When a charged-particle bunch in a storage ring is kicked to a large transverse offset, the time series describing the dynamics of the bunch centroid is determined both by the lattice focusing and by the Fourier transform of the 1D density profile of the bunch projected along the angle of the kick. In the presence of nonlinear focusing, we show that this fact can be exploited to enable 2D phase space reconstruction of the bunch (computational tomography) based only on turn-by-turn beam position monitor data. We demonstrate various tomography methods based on this principle, including machine learning methods, and discuss their advantages and disadvantages, and measure of reliability. We also mention a possible extension to 4D phase space computational tomography.