The full optimization of the design and operation of instruments whose functioning relies on
the interaction of radiation with matter is a super-human task, due to the large dimensionality …

Data-driven science and technology offer transformative tools and methods to science. This
review article highlights the latest development and progress in the interdisciplinary field of …

Particle accelerators are extremely complex machines that are challenging to simulate,
design, and control. Over the past decade, artificial intelligence (AI) and machine learning …

Particle accelerators are complex systems that focus, guide, and accelerate intense charged
particle beams to high energy. Beam diagnostics present a challenging problem due to …

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

The occurrence of vacuum arcs or radio frequency (rf) breakdowns is one of the most
prevalent factors limiting the high-gradient performance of normal conducting rf cavities in …

In this work, nondestructive virtual diagnostics are applied to retrieve the electron beam time
of arrival and energy in a relativistic ultrafast electron diffraction (UED) beamline using …

Next-generation accelerator concepts, which hinge on the precise shaping of beam
distributions, demand equally precise diagnostic methods capable of reconstructing beam …

When a charged-particle bunch in a storage ring is kicked to a large transverse offset, the
time series describing the dynamics of the bunch centroid is determined both by the lattice …

In this work, we develop a machine learning (ML) model with aleatoric uncertainty for the low
energy beam transport (LEBT) region of the LANSCE linear accelerator in which we model …