6 papers · 1 filter
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…
Autonomous operation of the DIAG0 diagnostic line for 6D phase-space monitoring at LCLS-II
Ryan Roussel, Gopika Bhardwaj, Dylan Kennedy +7
Characterizing the full 6-dimensional phase-space distribution of beams from the LCLS-II photoinjector is essential for understanding and optimizing downstream accelerator performa…
Deployment and validation of predictive 6-dimensional beam diagnostics through generative reconstruction with standard accelerator elements
Seongyeol Kim, Juan Pablo Gonzalez-Aguilera, Ryan Roussel +7
Understanding the 6-dimensional phase space distribution of particle beams is essential for optimizing accelerator performance. Conventional diagnostics such as use of transverse d…
Harnessing the Power of Gradient-Based Simulations for Multi-Objective Optimization in Particle Accelerators
Kishansingh Rajput, Malachi Schram, Auralee Edelen +6
Particle accelerator operation requires simultaneous optimization of multiple objectives. Multi-Objective Optimization (MOO) is particularly challenging due to trade-offs between t…
Efficient 6-dimensional phase space reconstruction from experimental measurements using generative machine learning
Ryan Roussel, Juan Pablo Gonzalez-Aguilera, Auralee Edelen +5
Next-generation accelerator concepts which hinge on the precise shaping of beam distributions, demand equally precise diagnostic methods capable of reconstructing beam distribution…
Multi-Objective Bayesian Active Learning for MeV-ultrafast electron diffraction
Fuhao Ji, Auralee Edelen, Ryan Roussel +13
Ultrafast electron diffraction using MeV energy beams(MeV-UED) has enabled unprecedented scientific opportunities in the study of ultrafast structural dynamics in a variety of gas,…