Machine learning for analysis of plasma driven Ion source

TL;DR

This paper applies machine learning techniques, specifically neural networks, to analyze and predict the behavior of a plasma-driven ion source used in accelerator physics, focusing on ion beam composition in hydrogen gas environments.

Contribution

It introduces the use of neural networks for analyzing ion beam data from a plasma-driven ion source, expanding previous studies with larger datasets and more comprehensive parameter analysis.

Findings

Neural networks successfully predict ion beam composition.

Enhanced understanding of ion source behavior with machine learning.

Improved analysis of ion beam properties over traditional methods.

Abstract

Recently, neural networks have found many applications in different fields including Genetics, Pharmacy, Astrophysics and High Energy Physics [1-3]. In the field of accelerator physics it has been used for control systems [4]. In this paper we present the results based on machine learning techniques motivated to predict the behaviour of ion source in terms of composition of the ion beam while using hydrogen gas to produce $H^+$ ions. In the framework of the stellarator type Figure-8 Storage Ring (F8SR) project, a volume type ion source was designed for the low energy ion beam transport experiments. In a first step the functioning of this ion source was studied and the results were published, but only small number of measurements were analysed as the main requirement for the on going experiment was fulfilled. Though at a later stage, more number of measurements were recorded with larger parameter space to investigate the properties of extracted ion beams from this source further. With recent interests and improved techniques in the applications of machine learning algorithms data analysis using neural network has been applied to study the ion beams from this ion source.