arXiv subjects

Frederick Cropp

Publications and source records attributed to Frederick Cropp.

5 recordsLinked to original sources

arXiv2026

FACET2-S2E: Start-to-end simulations of the FACET-II beamline

FACET2-S2E is a Python package for start-to-end simulations of the Facility for Advanced Accelerator Experimental Tests-II (FACET-II), a US Department of Energy National User Facility. A kilometer-long particle accelerator creates, manipulates, and accelerates electron beams to over 10 GeV before focusing and compressing them to the micron-scale. These beams create extreme electric and magnetic fields on the femtosecond timescale, uniquely enabling research into exotic states and advanced accelerator technology, including plasma wakefield acceleration. This software package enables present or prospective facility users to easily run the most common types of simulation pipelines to design experiments and interpret results.

physics.acc-ph↗

arXiv2023

Virtual-Diagnostic-Based Time Stamping for Ultrafast Electron Diffraction

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 technique has the potential to improve temporal resolution of pump and probe UED scans. Fluctuations in time of arrival have multiple components, including a shot-to-shot jitter and a long-term drift which can be separately addressed by closed loop feedback systems. A linear-regression-based model is used to fit the beam energy and time of arrival and is shown to be able to predict accurately behavior for both on long and short time scales. More advanced time-series analysis based on machine learning techniques can be applied to improve this prediction further.

physics.acc-ph↗

arXiv2021

Adaptive Latent Space Tuning for Non-Stationary Distributions

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 from images, mix them with scalar inputs within a general low-dimensional latent space, and then generate new complex 2D outputs which represent complex physical phenomenon. One important challenge faced by deep learning methods is large non-stationary systems whose characteristics change quickly with time for which re-training is not feasible. In this paper we present a method for adaptive tuning of the low-dimensional latent space of deep encoder-decoder style CNNs based on real-time feedback to quickly compensate for unknown and fast distribution shifts. We demonstrate our approach for predicting the properties of a time-varying charged particle beam in a particle accelerator whose components (accelerating electric fields and focusing magnetic fields) are also quickly changing with time.

stat.ML↗

arXiv2021

Adaptive deep learning for time-varying systems with hidden parameters: Predicting changing input beam distributions of compact particle accelerators

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 predictive capabilities of ML tools degrade as the systems are no longer accurately represented by the data sets with which the ML models were trained. Re-training is possible, but only if the changes are slow and if new input-output training data measurements can be made online non-invasively. In this work we present an approach to deep learning for time-varying systems in which adaptive feedback based only on available system output measurements is applied to encoded low-dimensional dense layers of encoder-decoder type CNNs. We demonstrate our method in developing an inverse model of a complex charged particle accelerator system, mapping output beam measurements to input beam distributions while both the accelerator components and the unknown input beam distribution quickly vary with time. We demonstrate our results using experimental measurements of the input and output beam distributions of the HiRES ultra-fast electron diffraction (UED) microscopy beam line at Lawrence Berkeley National Laboratory. We show how our method can be used to aid both physics and ML-based surrogate online models to provide non-invasive beam diagnostics and we also demonstrate how our method can be used to automatically track the time varying quantum efficiency map of a particle accelerator's photocathode.

physics.acc-ph↗

arXiv2020

Ultrafast optical melting of trimer superstructure in layered 1T'-TaTe2

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 use of advanced ultrafast probes sensitive to self-organised atomic-scale patterns. Here, we report the first ultrafast investigation of TaTe2, which exhibits unique charge and lattice trimer order characterised by a transition upon cooling from stripe-like chains into a $(3 \times 3)$ superstructure of trimer clusters. Utilising MeV-scale ultrafast electron diffraction, we capture the photo-induced TaTe2 structural dynamics -- exposing a rapid $\approx\!1.4$ ps melting of its low-temperature ordered state followed by recovery via thermalisation into a hot cluster superstructure. Density-functional calculations indicate that the initial quench is triggered by intra-trimer Ta charge transfer which destabilises the clusters, unlike melting of charge density waves in other TaX2 compounds. Our work paves the way for further exploration and ultimately rapid optical and electronic manipulation of trimer superstructures.

cond-mat.mtrl-sci↗