Longitudinal RF capture simulation and BPM signal estimation

TL;DR

This paper reviews the theory of longitudinal RF capture, simulates various capture scenarios, compares their efficiencies, and estimates BPM signals for heavy ion machines, achieving good agreement with measurements.

Contribution

It introduces a simulation program for RF capture processes considering different initial distributions and capture curves, and estimates BPM signals for heavy ion machines.

Findings

Gaussian adiabatic capture yields higher efficiency

Shorter bunch length with Gaussian adiabatic capture

Good agreement between estimated and measured BPM signals

Abstract

In this paper, the theoretical aspects behind longitudinal RF capture are reviewed and the capture process is simulated via a program based on this theory. Four kinds of cases with different initial distribution and capture curve are considered, i.e. uniform distribution with adiabatic capture, uniform distribution with non-adiabatic capture, Gaussian distribution with adiabatic capture and Gaussian distribution with non-adiabatic capture. The simulation results are compared each other and discussed, and Gaussian distribution with adiabatic capture is demonstrated having a higher capture efficiency and leading to a shorter bunch length. In addition, the BPM induced signal is simulated with high input impendence, i.e. $1M\Omega$, and low input impendence, i.e. $50\Omega$, respectively. Finally, the BPM signal of Heavy Ion Medical Machine (HIMM) is estimated and compared with measured one, and a good agreement is achieved.