Developing Neural Networks for Accelerator Beam Phase Space Tomography

High-energy particle accelerators require an in-depth understanding of the beam properties to
operate effectively. The characteristics of beams have been studied using a variety of methods,
but as accelerator machines become increasingly advanced, more sophisticated techniques are
required to understand their behaviour. Modern diagnostic and analysis methods often demand
the processing of large amounts of data, which involves significant computational power and
run-time to complete a given analysis, limiting the applicability of some traditional techniques.
This PhD project aims to develop artificial intelligence and machine learning methods for
characterising particle accelerators and beam behaviour. These methods offer the possibility to
greatly improve accelerator tuning and operation efficiency by processing the large data sets
required with a fraction of the computing resources.
As an example, machine learning has been applied to measure the charge distribution within
bunches of high-energy electrons using CLARA, the Compact Linear Accelerator for Research
and Applications at Daresbury Laboratory [1]. The use of machine learning for phase space
tomography allows the reconstruction of the phase space distribution of the electron bunches by
training neural networks to recover the distribution from beam images.
The computing requirements for traditional phase space tomography techniques increase rapidly
with increasing dimensionality of the phase space. The storage of a D-dimensional distribution in
an array with dimension length N requires a data set structure of ND values, and the processing of
this data to reconstruct the phase space can require considerable memory resources. The amount
of memory required to store a data set representing four-dimensional phase-space distributions
with a moderate resolution of N < 100 pixels can be a several gigabytes.
Image compression techniques allow for the reduction of the processing time and data storage
requirements...