Frederick Cropp
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View article: High-Performance Accelerator Modeling: Toward Improving Controls and Diagnostics for High-Brightness Beams in Experiment
High-Performance Accelerator Modeling: Toward Improving Controls and Diagnostics for High-Brightness Beams in Experiment Open
One of the main charges of beam physics is to improve the overall beam quality delivered to experiments and applications, which is generally quantified by the beam brightness. High brightness beams have numerous applications, including x-r…
View article: FAST Low-Energy Beamline Studies: Toward High-Peak 5D Brightness Beams for FAST-GREENS
FAST Low-Energy Beamline Studies: Toward High-Peak 5D Brightness Beams for FAST-GREENS Open
The FAST beamline is the injector for the planned Gamma-Ray Electron ENhanced Source (GREENS) program, which aims to achieve the demonstration and first application of a high-efficiency, high-average-power free-electron laser at 515 nm. FA…
View article: Relativistic ultrafast electron diffraction at high repetition rates
Relativistic ultrafast electron diffraction at high repetition rates Open
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 science. The last couple of decades have witnessed ultrafast …
View article: Ultrafast optical melting of trimer superstructure in layered 1T'-TaTe<sub>2</sub>
Ultrafast optical melting of trimer superstructure in layered 1T'-TaTe<sub>2</sub> Open
Quasi-two-dimensional transition-metal dichalcogenides are a key platform for exploring emergent nanoscale phenomena arising from complex interactions. Access to the underlying degrees-of-freedom on their natural time scales motivates the …
View article: Relativistic ultrafast electron diffraction at high repetition rates
Relativistic ultrafast electron diffraction at high repetition rates Open
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 science. The last couple of decades have witnessed ultrafast …
View article: Virtual-diagnostic-based time stamping for ultrafast electron diffraction
Virtual-diagnostic-based time stamping for ultrafast electron diffraction Open
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 independently measured machine parameters. This tec…
View article: Adaptive autoencoder latent space tuning for more robust machine learning beyond the training set for six-dimensional phase space diagnostics of a time-varying ultrafast electron-diffraction compact accelerator
Adaptive autoencoder latent space tuning for more robust machine learning beyond the training set for six-dimensional phase space diagnostics of a time-varying ultrafast electron-diffraction compact accelerator Open
We present a general adaptive latent space tuning approach for improving the robustness of machine learning tools with respect to time variation and distribution shift. We demonstrate our approach by developing an encoder-decoder convoluti…
View article: Virtual-Diagnostic-Based Time Stamping for Ultrafast Electron Diffraction
Virtual-Diagnostic-Based Time Stamping for Ultrafast Electron Diffraction Open
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) beamline using independently-measured machine parameters. This te…
View article: 6D Phase space diagnostics based on adaptively tuned physics-informed generative convolutional neural networks
6D Phase space diagnostics based on adaptively tuned physics-informed generative convolutional neural networks Open
A physics-informed generative convolutional neural network (CNN)-based 6D phase space diagnostic is presented which generates all 15 unique 2D projections ( x, y ), ( x, y′ ),...,( z, E ) of a charged particle beam’s 6D phase space ( x, y,…
View article: An adaptive approach to machine learning for compact particle accelerators
An adaptive approach to machine learning for compact particle accelerators Open
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 predictive capabilities of ML tools degrade if the systems are n…
View article: Ultrafast optical melting of trimer superstructure in layered 1T′-TaTe2
Ultrafast optical melting of trimer superstructure in layered 1T′-TaTe2 Open
View article: Adaptive Latent Space Tuning for Non-Stationary Distributions
Adaptive Latent Space Tuning for Non-Stationary Distributions Open
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-decoder deep CNNs are able to extract features directly fr…
View article: Adaptive Deep Learning for Time-Varying Systems With Hidden Parameters: Predicting Changing Input Beam Distributions of Compact Particle Accelerators
Adaptive Deep Learning for Time-Varying Systems With Hidden Parameters: Predicting Changing Input Beam Distributions of Compact Particle Accelerators Open
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 predictive capabilities of ML tools degrade if the systems are no…
View article: Adaptive deep learning for time-varying systems with hidden parameters: Predicting changing input beam distributions of compact particle accelerators
Adaptive deep learning for time-varying systems with hidden parameters: Predicting changing input beam distributions of compact particle accelerators Open
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 directly from raw data. For time-varying systems the predict…
View article: Demonstration of adaptive machine learning-based distribution tracking on a compact accelerator: Towards enabling model-based 6D non-invasive beam diagnostics
Demonstration of adaptive machine learning-based distribution tracking on a compact accelerator: Towards enabling model-based 6D non-invasive beam diagnostics Open
Intense charged particle beam dynamics are dominated by complex nonlinear collective effects such as space charge forces and coherent synchrotron radiation in a 6 dimensional (6D) phase space (x,y,z,px,py,pz). Non-invasive 6D phase space d…
View article: Analysis of Skew Quadrupole Compensation in RF-Photoinjectors
Analysis of Skew Quadrupole Compensation in RF-Photoinjectors Open
In this paper, we present a detailed analysis of the effects of quadrupole components in radiofrequency (RF) photoinjectors, which could occur either in the RF cavity or in the gun solenoid, and their compensation using a skew quadrupole c…
View article: Electron Ghost Imaging
Electron Ghost Imaging Open
In this Letter we report a demonstration of electron ghost imaging. A digital micromirror device directly modulates the photocathode drive laser to control the transverse distribution of a relativistic electron beam incident on a sample. C…