Two-stage Convolutional Neural Network for pseudo six-dimensional phase space reconstruction

TL;DR

This paper presents a two-stage CNN that reconstructs the 6D beam phase space from limited 2D images, offering a faster and more resource-efficient alternative to traditional tomography methods.

Contribution

The authors develop a novel two-stage CNN model trained on simulation data to reconstruct pseudo 6D phase space from minimal transverse images in particle accelerators.

Findings

Reconstructed 6D phase space distribution consistent with measurements at KEK-ATF.

Significantly reduces measurement time compared to tomography.

Requires less computational resources than existing techniques.

Abstract

In particle accelerators, broad characterization of the six-dimensional (6D) beam phase space is crucial but difficult to obtain with conventional beam diagnostics. We develop a two-stage convolutional neural network (CNN) that reconstructs the 6D phase space from only sixteen transverse $x-y$ screen images taken at a place with dispersion by different phase space rotation angles. The model is trained with simulation data of KEK-Accelerator Test Facility (ATF) injector with ASTRA. The real-space images in the chicane orbit at the KEK-ATF injector were acquired by varying the RF phase of the RF electron gun and the solenoid magnetic field. From these data, we reconstructed a pseudo 6D phase space distribution at the cathode surface, expressed through 15 two-dimensional (2D) distributions covering all pairwise coordinate combinations. The time width and spatial spread of the electron beam at the cathode showed values consistent with the measured values at KEK-ATF. Compared to existing 6D beam imaging measurement techniques such as tomography, it significantly reduces measurement time and required computational resources, enabling the provision of a more practical 6D phase space measurement method.