14 citations · 28 across the 7 of their papers we have counts for
7 papers
Relativistic ultrafast electron diffraction at high repetition rates
K. M. Siddiqui, D. B. Durham, F. Cropp +17
The ability to resolve the dynamics of matter on its native temporal and spatial scales constitutes a key challenge and convergent theme across chemistry, biology, and materials sc…
Virtual-Diagnostic-Based Time Stamping for Ultrafast Electron Diffraction
Frederick Cropp, Lauren Moos, Alexander Scheinker +6
In this work, non-destructive virtual diagnostics are applied to retrieve the electron beam time of arrival and energy in a relativistic ultrafast electron diffraction (UED) beamli…
The LCLS-II Gun & Buncher LLRF Controller Upgrade
Christos Bakalis, Andrew Benwell, Daron Chabot +6
LCLS-II is currently in its commissioning phase at SLAC. It is an X-ray FEL driven by a CW superconducting LINAC. The beam injector plays a crucial role in the overall performance…
Digital Low-Level RF control system for Accumulator Ring at Advanced Light Source Upgrade Project
Qiang Du, Shreeharshini Murthy, Michael Betz +8
Currently ALS is undergoing an upgrade to ALSU to produce 100 times brighter soft X-ray light. The LLRF system for Accumulator Ring (AR) is composed of two identical LLRF stations,…
LCLS-II-HE verification cryomodule high gradient performance and quench behavior
S. Posen, A. Cravatta, M. Checchin +43
An 8-cavity, 1.3 GHz, LCLS-II-HE cryomodule was assembled and tested at Fermilab to verify performance before the start of production. Its cavities were processed with a novel nitr…
Adaptive Latent Space Tuning for Non-Stationary Distributions
Alexander Scheinker, Frederick Cropp, Sergio Paiagua +1
Powerful deep learning tools, such as convolutional neural networks (CNN), are able to learn the input-output relationships of large complicated systems directly from data. Encoder…
Adaptive deep learning for time-varying systems with hidden parameters: Predicting changing input beam distributions of compact particle accelerators
Alexander Scheinker, Frederick Cropp, Sergio Paiagua +1
Machine learning (ML) tools such as encoder-decoder deep convolutional neural networks (CNN) are able to extract relationships between inputs and outputs of large complex systems d…