Particle accelerators are host to myriad nonlinear and complex physical phenomena. They
often involve a multitude of interacting systems, are subject to tight performance demands …

Machine learning (ML) is growing in popularity for various particle accelerator applications
including anomaly detection such as faulty beam position monitor or RF fault identification …

Abstract Machine learning (ML) tools such as encoder-decoder convolutional neural
networks (CNN) can represent incredibly complex nonlinear functions which map between …

At present, a variety of image-based diagnostics are used in particle accelerator systems.
Often times, these are viewed by a human operator who then makes appropriate …

This paper presents a novel approach for constructing neural networks which model
charged particle beam dynamics. In our approach, the Taylor maps arising in the …

Abstract Machine learning (ML) tools are able to learn relationships between the inputs and
outputs of large complex systems directly from data. However, for time-varying systems, the …

Machine learning (ML) is a subfield of artificial intelligence. The term applies broadly to a
collection of computational algorithms and techniques that train systems from raw data …

Two types of neural network beauty trigger architecture, based on identification of electrons
in jets and recognition of secondary vertices, have been simulated in the environment of the …

We present a new model-independent dynamic feedback technique, rotation rate tuning, for
automatically and simultaneously tuning coupled components of uncertain, complex …

With the growth of machine learning models and neural networks in measurement and
control systems comes the need to deploy these models in a way that is compatible with …