151 citations · 464 across the 35 of their papers we have counts for
38 papers
AI Acceleration of Muon Collider R&D: A Report from the AI4MuC Workshop
J. Scott Berg, David Bizzozero, Tulika Bose +27
A Muon Collider is one of the most attractive routes to multi-TeV lepton collisions, but it requires the accelerator and detector communities to solve a tightly coupled set of prob…
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…
Physics-Guided Inverse Design of Optical Waveforms for Nonlinear Electromagnetic Dynamics
Hao Zhang, Jack Hirschman, Randy Lemons +7
Structured optical waveforms are emerging as powerful control fields for the next generation of complex photonic and electromagnetic systems, where the temporal structure of light…
Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)
Julia Gonski, Jennifer Ott, Jenni Ott +129
The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environmen…
Data Driven Drift Correction For Complex Optical Systems
Aashwin Mishra, Matt Seaberg, Ryan Roussel +4
To exploit the thousand-fold increase in spectral brightness of modern light sources, increasingly intricate experiments are being conducted that demand extremely precise beam traj…
A Start To End Machine Learning Approach To Maximize Scientific Throughput From The LCLS-II-HE
Aashwin Mishra, Matt Seaberg, Ryan Roussel +5
With the increasing brightness of Light sources, including the Diffraction-Limited brightness upgrade of APS and the high-repetition-rate upgrade of LCLS, the proposed experiments…
Outlook Towards Deployable Continual Learning for Particle Accelerators
Kishansingh Rajput, Sen Lin, Auralee Edelen +2
Particle Accelerators are high power complex machines. To ensure uninterrupted operation of these machines, thousands of pieces of equipment need to be synchronized, which requires…
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…
Emittance Minimization for Aberration Correction II: Physics-informed Bayesian Optimization of an Electron Microscope
Desheng Ma, Steven E. Zeltmann, Chenyu Zhang +6
Aberration-corrected Scanning Transmission Electron Microscopy (STEM) has become an essential tool in understanding materials at the atomic scale. However, tuning the aberration co…
Emittance Minimization for Aberration Correction I: Aberration correction of an electron microscope without knowing the aberration coefficients
Desheng Ma, Steven E. Zeltmann, Chenyu Zhang +6
Precise alignment of the electron beam is critical for successful application of scanning transmission electron microscopes (STEM) to understanding materials at atomic level. Despi…
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,…
Leveraging Prior Mean Models for Faster Bayesian Optimization of Particle Accelerators
Tobias Boltz, Jose L. Martinez, Connie Xu +7
Tuning particle accelerators is a challenging and time-consuming task that can be automated and carried out efficiently using suitable optimization algorithms, such as model-based…
Four-Dimensional Phase-Space Reconstruction of Flat and Magnetized Beams Using Neural Networks and Differentiable Simulations
Seongyeol Kim, Juan Pablo Gonzalez-Aguilera, Philippe Piot +9
Beams with cross-plane coupling or extreme asymmetries between the two transverse phase spaces are often encountered in particle accelerators. Flat beams with large transverse-emit…
Bayesian Optimization Algorithms for Accelerator Physics
Ryan Roussel, Auralee L. Edelen, Tobias Boltz +23
Accelerator physics relies on numerical algorithms to solve optimization problems in online accelerator control and tasks such as experimental design and model calibration in simul…
Demonstration of Autonomous Emittance Characterization at the Argonne Wakefield Accelerator
Ryan Roussel, Auralee Edelen, Dylan Kennedy +3
Transverse beam emittance plays a key role in the performance of high brightness accelerators. Characterizing beam emittance is often done using a quadrupole scan, which fits beam…
Applications of Differentiable Physics Simulations in Particle Accelerator Modeling
Ryan Roussel, Auralee Edelen
Current physics models used to interpret experimental measurements of particle beams require either simplifying assumptions to be made in order to ensure analytical tractability, o…
Neural Network Prior Mean for Particle Accelerator Injector Tuning
Connie Xu, Ryan Roussel, Auralee Edelen
Bayesian optimization has been shown to be a powerful tool for solving black box problems during online accelerator optimization. The major advantage of Bayesian based optimization…
Multipoint-BAX: A New Approach for Efficiently Tuning Particle Accelerator Emittance via Virtual Objectives
Sara A. Miskovich, Willie Neiswanger, William Colocho +7
Although beam emittance is critical for the performance of high-brightness accelerators, optimization is often time limited as emittance calculations, commonly done via quadrupole…
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…
Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper
Tommaso Dorigo, Andrea Giammanco, Pietro Vischia +33
The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a super-human task, given the large dimensi…
Neural Network Solver for Coherent Synchrotron Radiation Wakefield Calculations in Accelerator-based Charged Particle Beams
Auralee Edelen, Christopher Mayes
Particle accelerators support a wide array of scientific, industrial, and medical applications. To meet the needs of these applications, accelerator physicists rely heavily on deta…
C Demonstration Research and Development Plan
Emilio A. Nanni, Martin Breidenbach, Caterina Vernieri +46
C is an opportunity to realize an ee collider for the study of the Higgs boson at GeV, with a well defined upgrade path to 550 GeV while staying on the…