9 citations · 21 across the 5 of their papers we have counts for
6 papers · 1 filter
Introduction to Machine Learning for Accelerator Physics
Daniel Ratner
This pair of CAS lectures gives an introduction for accelerator physics students to the framework and terminology of machine learning (ML). We start by introducing the language of…
Online tuning and light source control using a physics-informed Gaussian process Adi
A. Hanuka, J. Duris, J. Shtalenkova +4
Operating large-scale scientific facilities often requires fast tuning and robust control in a high dimensional space. In this paper we introduce a new physics-informed optimizatio…
Temporal X-ray Reconstruction using Temporal and Spectral Measurements at LCLS
Florian Christie, Alberto Andrea Lutman, Yuantao Ding +7
Transverse deflecting structures (TDS) are widely used in accelerator physics to measure the longitudinal density of particle bunches. When used in combination with a dispersive se…
Mapping Photocathode Quantum Efficiency with Ghost Imaging
K. Kabra, S. Li, F. Cropp +3
Measuring the quantum efficiency (QE) map of a photocathode injector typically requires laser scanning, an invasive operation that involves modifying the injector laser focus and r…
Bayesian optimization of a free-electron laser
Joseph Duris, Dylan Kennedy, Adi Hanuka +5
The Linac Coherent Light Source changes configurations multiple times per day, necessitating fast tuning strategies to reduce setup time for successive experiments. To this end, we…
Opportunities in Machine Learning for Particle Accelerators
Auralee Edelen, Christopher Mayes, Daniel Bowring +10
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…