A5069300874- Particle accelerators and beam dynamics
- Particle Accelerators and Free-Electron Lasers
- Magnetic Properties and Applications
- Magnetic confinement fusion research
- Electric Motor Design and Analysis
- Microfluidic and Capillary Electrophoresis Applications
- Superconducting Materials and Applications
- Nuclear reactor physics and engineering
- Scientific Computing and Data Management
- Nuclear Physics and Applications
- Advanced Multi-Objective Optimization Algorithms
- Solidification and crystal growth phenomena
- Real-time simulation and control systems
- Advanced X-ray Imaging Techniques
- Non-Destructive Testing Techniques
- Spectroscopy Techniques in Biomedical and Chemical Research
- Gaussian Processes and Bayesian Inference
University of Chicago
2021-2024
Fermi National Accelerator Laboratory
2024
SLAC National Accelerator Laboratory
2022-2023
Characterizing the phase space distribution of particle beams in accelerators is a central part understanding beam dynamics and improving accelerator performance. However, conventional analysis methods either use simplifying assumptions or require specialized diagnostics to infer high-dimensional (>2D) properties. In this Letter, we introduce general-purpose algorithm that combines neural networks with differentiable tracking efficiently reconstruct distributions without using manipulations....
Next-generation accelerator concepts, which hinge on the precise shaping of beam distributions, demand equally diagnostic methods capable reconstructing distributions within six-dimensional position-momentum spaces. However, characterization intricate features using current techniques necessitates a substantial number measurements, many hours valuable time. Novel phase space reconstruction are needed to reduce measurements required reconstruct detailed, high-dimensional in order resolve...
Beams with cross-plane coupling or extreme asymmetries between the two transverse phase spaces are often encountered in particle accelerators. Flat beams large transverse-emittance ratios critical for future linear colliders. Similarly, magnetized significant expected to enhance performance of electron cooling hadron beams. Preparing these requires precise control and characterization four-dimensional space. In this study, we employ generative phase-space reconstruction techniques rapidly...
Future improvements in particle accelerator performance are predicated on increasingly accurate online modeling of accelerators. Hysteresis effects magnetic, mechanical, and material components accelerators often neglected models used to inform control algorithms, even though reproducibility errors from systems exhibiting hysteresis not negligible high precision In this Letter, we combine the classical Preisach model with machine learning techniques efficiently create nonparametric,...
Particle accelerators are invaluable discovery engines in the chemical, biological and physical sciences. Characterization of accelerated beam response to accelerator input parameters is of-ten first step when conducting accelerator-based experiments. Currently used techniques for characterization, such as grid-like parameter sampling scans, become impractical extended higher dimensional spaces, complicated measurement constraints present, or prior information known about scarce. In this...
Beams with cross-plane coupling or extreme asymmetries between the two transverse phase spaces are often encountered in particle accelerators. Flat beams large transverse-emittance ratios critical for future linear colliders. Similarly, magnetized significant expected to enhance performance of electron cooling hadron beams. Preparing these requires precise control and characterization four-dimensional space. In this study, we employ generative space reconstruction (GPSR) techniques rapidly...
Next-generation accelerator concepts which hinge on the precise shaping of beam distributions, demand equally diagnostic methods capable reconstructing distributions within 6-dimensional position-momentum spaces. However, characterization intricate features using conventional techniques necessitates hundreds measurements, many hours valuable time. Novel phase space reconstruction are needed to substantially reduce number measurements required reconstruct detailed, high-dimensional in order...
Characterizing the phase space distribution of particle beams in accelerators is a central part accelerator understanding and performance optimization. However, conventional reconstruction-based techniques either use simplifying assumptions or require specialized diagnostics to infer high-dimensional ($>$ 2D) beam properties. In this Letter, we introduce general-purpose algorithm that combines neural networks with differentiable tracking efficiently reconstruct distributions without using...