Conveners
Optimisation and Control
- Michael Schenk (CERN)
Optimisation and Control
- Seongyeol Kim
Optimisation and Control
- Jason St John (Fermi National Accelerator Laboratory)
The High Energy Photon Source (HEPS) is a fourth-generation synchrotron radiation facility under construction in Beijing, China. Since the beam commissioning of the storage ring commenced in July 2024, progress has proceeded smoothly, and the first light was achieved in October. During the construction and beam commissioning of HEPS, we explored machine learning to address critical technical...
Large-scale facilities like European XFEL consist of a multitude of subsystems, which often require frequent calibration. Additionally, accurate real-time tuning of many of these subsystems is critical to maintain stable and optimal performance. Automation techniques can be leveraged to reduce operators' time investment and potentially increase the exploitation of allotted beamtime, both in...
The Advanced Photon Source (APS) facility has just completed an upgrade to become one of the world’s brightest storage-ring light sources. For the first time, machine learning (ML) methods have been extensive used as part of the baseline commissioning plan. Most popular such method was Bayesian optimization (BO) – a tool for efficient online high-dimensional single and multi-objective tuning....
Feedback control is an essential component for the successful operation of particle accelerators. However, achieving the desired closed-loop performance requires precise model knowledge, which is difficult to obtain in complex accelerator systems. For this reason, we present an application of a combined optimization approach that estimates the response matrix online while optimizing the chosen...
Particle accelerators play a critical role in modern scientific research. However, existing manual beam control methods heavily rely on experienced operators, leading to significant time consumption and potential challenges in managing next-generation accelerators characterized by higher beam current and stronger nonlinear properties. In this paper, we establish a dynamical foundation for...
Heavy ion synchrotrons, like the SIS18 at GSI, rely on the proven numerical approaches to correct the closed orbit. The SIS18 has a relative moderate amount of BPMs (one per cell) and requires a well corrected and known orbit, especially near the injection/extraction systems. Fluctuations of the BPM signal arise from the electronics. In addition there are systematic errors due to the relative...
A general Bayesian optimisation tool is being developed at Diamond Light Source to improve machine performance by constructing surrogates from Gaussian Process (GP) models. Priors are placed on covariance kernel hyperparameters to guide an optimiser and prevent overfitting. The model has been integrated with the machine control system. During an experiment aimed at improving injection...
Power supply ripples at various frequencies - characteristic to the magnet circuits or from the electrical network - have always been an issue in accelerator operations, with several mitigation measures put in place over the years. This contribution summarises recent efforts in the CERN SPS to compensate the ripple at 50 Hz and its harmonics in the main quadrupole circuits with ML methods. It...
The Linear IFMIF Prototype Accelerator (LIPAc) is designed to accelerate 125 mA of D+ to 9 MeV in CW. The very high power stored in the beam (~1.1 MW) and the use of superconductive RF cavities requires precise control of beam losses (target <1e-6). On the other hand the intense beam is affected by strong space charge forces that easily results in significant halo formation. This contribute is...
This paper presents a novel application of Multipoint Bayesian Algorithmic Execution (multipointBAX) to optimize dynamic aperture (DA) and momentum aperture (MA) in lattice design. DAMA optimization is a critical design task for storage rings, ultimately determining the flux of x-ray sources and luminosity of colliders. Traditionally, solving this multi-objective optimization problem has...
The goal of machine learning for accelerator control is to automate the start-up, optimization, and execution of experiments at accelerator facilities with limited-to-no human operator input. To address this challenge, we have been pursuing a research program to completely automate sequential accelerator beamline configuration tasks at the Argonne Wakefield Accelerator (AWA). These tasks...
Particle accelerator operation requires simultaneous optimization of
multiple objectives. Multi-Objective Optimization (MOO) is particularly challenging
due to trade-offs between the objectives. Evolutionary algorithms, such as genetic
algorithm (GA), have been leveraged for many optimization problems, however, they
do not apply to complex control problems by design. This paper...
Complex accelerators must have control systems that can handle dynamic nonlinear environments. This makes traditional control methods unsuitable as they can struggle to adapt to these uncertainties. This provides an ideal environment for reinforcement learning algorithms as they are adaptable and generalizable. We present a reinforcement learning pipeline that can effectively handle the...
We present a reinforcement learning (RL) framework for controlling particle accelerated experiments that builds explainable physics-based constraints on agent behavior. The goal is to increase transparency and trust by letting users verify that the agent's decision-making process incorporates suitable physics. Our algorithm uses a learnable surrogate function for physical observables, such as...
