Publications (11)
Improving Surrogate Model Accuracy for the LCLS-II Injector Frontend Using Convolutional Neural Networks and Transfer Learning
Lipi Gupta, Auralee Edelen, Nicole Neveu +3
Machine learning models of accelerator systems (`surrogate models') are able to provide fast, accurate predictions of accelerator physics phenomena. However, approaches to date typ…
Exact 1-D Model for Coherent Synchrotron Radiation with Shielding and Bunch Compression
Christopher Mayes, Georg Hoffstaetter
Coherent Synchrotron Radiation has been studied effectively using a 1-dimensional model for the charge distribution in the realm of small angle approximations and high energies. He…
Phase Space Reconstruction from Accelerator Beam Measurements Using Neural Networks and Differentiable Simulations
Ryan Roussel, Auralee Edelen, Christopher Mayes +5
Characterizing the phase space distribution of particle beams in accelerators is a central part of accelerator understanding and performance optimization. However, conventional rec…
The Cornell-BNL FFAG-ERL Test Accelerator: White Paper
Ivan Bazarov, John Dobbins, Bruce Dunham +20
The Cornell-BNL FFAG-ERL Test Accelerator (C) will comprise the first ever Energy Recovery Linac (ERL) based on a Fixed Field Alternating Gradient (FFAG) lattice. In particular…
Machine Learning for Orders of Magnitude Speedup in Multi-Objective Optimization of Particle Accelerator Systems
Auralee Edelen, Nicole Neveu, Yannick Huber +3
High-fidelity physics simulations are powerful tools in the design and optimization of charged particle accelerators. However, the computational burden of these simulations often l…
Expanding LUME to Support Virtual Accelerators and Digital Twins
Ryan Roussel, Christopher M. Pierce, Sara Miskovich +5
Virtual accelerators and digital twins are increasingly essential tools for accelerator operations, controls development and verification, and model-based optimization. However, cu…