Auralee Edelen

TL;DR: In this paper, a Gaussian process model is used to predict the machine response with respect to control parameters, enabling a balance of exploration and exploitation in the search for the global optimum.

TL;DR: In this article, the authors used machine learning methods to predict the longitudinal phase space (LPS) distribution of particle accelerators using only nondestructive linac and e-beam measurements as inputs.

TL;DR: In this paper, a neural network-based control system for particle accelerators is described. And the authors describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator controller systems, and describe a neural networks based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility.

TL;DR: In this paper, the authors introduce an approach based on machine learning to create nonlinear, fast-executing surrogate models that are informed by a sparse sampling of the physics simulation, which enables new ways for high-fidelity particle accelerator simulations to be used, at comparatively little computational cost.

TL;DR: This work reports on a first of its kind combination of automatic, model-independent feedback with a neural network for control of the longitudinal phase space of relativistic electron beams with femtosecond resolution based only on transverse deflecting cavity measurements.