Speaker
Description
The six-imensional (6D) phase space distribution of beam is an extremely important indicator of beam performance and provides useful information for understanding the actual state of the accelerator. On the other hand, the beam diagnostics for the 6D phase space is generally difficult and only a projection on a 1D or 2D phase space is usually obtained. We developed an algorithm based on Convolutional Neural Network (CNN) to reconstruct the 6D phase space
with a limited number of transverse beam images in $x-y$ plane. The advantage of this method is that it does not require as many computing resources as conventional back projection techniques. In this presentation, we show through simulation that a six-dimensional phase space can be reconstructed only from 4+4 beam images. An experimental study of the 6D phase space reconstruction in KEK-ATF is also presented.
| In which format do you inted to submit your paper? | LaTeX |
|---|