Conveners
Optimization & Control: Optimization & Control 1
- Auralee Edelen (SLAC, Stanford)
- Tetsuhiko Yorita (Research Center for Nuclear Physics, Osaka University)
Optimization & Control: Optimization & Control 2
- Tetsuhiko Yorita (Research Center for Nuclear Physics, Osaka University)
- Auralee Edelen (SLAC, Stanford)
Optimization & Control: Optimization & Control 3
- Auralee Edelen (SLAC, Stanford)
- Tetsuhiko Yorita (Research Center for Nuclear Physics, Osaka University)
C. Elliott, W. Blokland, D. Brown, B. Cathey, B. Maldonaldo Puente, C. Peters, K. Rajput, J. Rye, M. Schram, S. Thomas, A. Zhukov
Spallation Neutron Source, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
*Thomas Jefferson National Laboratory, Newport News, VA, 23606, USA
The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL), a high-power H- linear...
At a heavy ion linac facility, such as ATLAS at Argonne National Laboratory, a new ion beam is tuned once or twice a week. The use of machine learning can be leveraged to streamline the tuning process, reducing the time needed to tune a given beam and allowing more beam time for the experimental program. After establishing automatic data collection and two-way communication with the control...
Bayesian optimization using Gaussian processes is a powerful tool to automate complex and time-consuming accelerator tuning tasks and has been demonstrated to outperform conventional methods at several facilities. In high-dimensional input spaces, however, even this sample efficient search may take a prohibitively large number of steps to reach convergence. In this contribution, we discuss the...
When designing a laser-plasma acceleration setup, it is common to explore the parameter space (plasma density, laser intensity, focal position, etc.) with Particle-In-Cell (PIC) simulations in order to find an optimal configuration that, for example, minimizes the energy spread or emittance of the accelerated beam. However, PIC simulations can be computationally expensive. Various reduced...
Over recent years, Bayesian optimization has become a widely adopted tool for fine-tuning and enhancing the operational performance of particle accelerators. While many Bayesian optimization (BO) algorithms focus on unconstrained optimization, constraints play an important role in accelerator operations. They ensure the safe functioning of the equipment and prevent damage to expensive...
Superconducting linear accelerators play a vital role in advancing scientific discoveries by requiring frequent reconfiguration and tuning. Minimizing setup time is crucial to maximize experimental time. Recently, reinforcement learning (RL) algorithms have emerged as effective tools for solving complex control tasks across various domains. Nonetheless, deploying RL agents trained in simulated...
The LCLS-II is a high repetition rate upgrade to the Linac Coherent Light Source (LCLS). LCLS-II will provide up to a million pulses per second to photon science users. The emittance and dark current are both critical parameters to optimize for ideal system performance. The initial commissioning of the LCLS-II injector was substantially aided by detailed online physics modeling linking high...
Reinforcement Learning (RL) is a promising approach for the autonomous AI-based control of particle accelerators. Real-time requirements for these algorithms can often not be satisfied with conventional hardware platforms.
In this contribution, the unique KINGFISHER platform being developed at KIT will be presented. Based on the novel AMD-Xilinx Versal platform, this system provides...
