The equilibrium emittance of the Pb beam in the CERN Low Energy Ion Ring (LEIR) results from the interplay of electron cooling and heating processes, as intra-beam scattering and space charge. In ...this paper we present the measurements of the emittance evolution as a function of intensity, working point and resonance excitation, and compare them with the simulations of the heating processes. Optimum settings for normal and skew sextupoles have been found for the compensation of resonances excited by the lattice.
Space Charge studies on LEIR Saa Hernandez, A.; Moreno, D.; Bartosik, H. ...
Journal of physics. Conference series,
09/2018, Letnik:
1067, Številka:
6
Journal Article
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The performance of the CERN Low Energy Ion Ring with electron cooled ion beams is presently limited by losses occurring once the beam has been captured in the RF buckets. An intense machine study ...program was started by the end of 2015 to mitigate the losses and improve the performance of the accelerator. The measurements pointed to the interplay of direct space charge forces and excited betatron resonances as the most plausible driving mechanism of these losses. In this paper, we present the systematic space-charge measurements performed in 2017 and compare them to space-charge tracking simulations based on an adaptive frozen potential.
The use of transversely split beams was proposed some years ago as a means to perform extraction from a circular particle accelerator over multiple turns. In the course of studies carried out to ...increase understanding of the beam behavior, space charge effects have been probed. The experimental results showed a dependence of the beamlets’ positions on the total beam intensity. In this paper detailed numerical simulations studies are reported, which clearly indicate that the observed behavior is due to indirect space charge effects. The analysis includes configurations, which have not yet been experimentally probed, in order to better understand the complex interplay between nonlinear single-particle and intensity-dependent effects.
We discuss the possibility of creating novel research tools by producing and storing highly relativistic beams of highly ionised atoms in the CERN accelerator complex, and by exciting their atomic ...degrees of freedom with lasers to produce high-energy photon beams. Intensity of such photon beams would be by several orders of magnitude higher than offered by the presently operating light sources, in the particularly interesting gamma-ray energy domain of 0.1-400 MeV. In this energy range, the high-intensity photon beams can be used to produce secondary beams of polarised electrons, polarised positrons, polarised muons, neutrinos, neutrons and radioactive ions. New research opportunities in a wide domain of fundamental and applied physics can be opened by the Gamma Factory scientific programme based on the above primary and secondary beams.
Numerical optimization algorithms are already established tools to increase and stabilize the performance of particle accelerators. These algorithms have many advantages, are available out of the ...box, and can be adapted to a wide range of optimization problems in accelerator operation. The next boost in efficiency is expected to come from reinforcement learning algorithms that learn the optimal policy for a certain control problem and hence, once trained, can do without the time-consuming exploration phase needed for numerical optimizers. To investigate this approach, continuous model-free reinforcement learning with up to 16 degrees of freedom was developed and successfully tested at various facilities at CERN. The approach and algorithms used are discussed and the results obtained for trajectory steering at the AWAKE electron line and LINAC4 are presented. The necessary next steps, such as uncertainty aware model-based approaches, and the potential for future applications at particle accelerators are addressed.
The attainment of a satisfactory operating point is one of the main problems in the tuning of particle accelerators. These are extremely complex facilities, characterized by the absence of a model ...that accurately describes their dynamics, and by an often persistent noise which, along with machine drifts, affects their behaviour in unpredictable ways. In this paper, we propose an online iterative Linear Quadratic Regulator (iLQR) approach to tackle this problem on the FERMI free-electron laser of Elettra Sincrotrone Trieste. It consists of a model identification performed by a neural network trained on data collected from the real facility, followed by the application of the iLQR in a Model-Predictive Control fashion. We perform several experiments, training the neural network with increasing amount of data, in order to understand what level of model accuracy is needed to accomplish the task. We empirically show that the online iLQR results, on average, in fewer steps than a simple gradient ascent (GA), and requires a less accurate neural network to achieve the goal.
This paper presents a comparison between two well-known deep Reinforcement Learning (RL) algorithms: Deep Q-Learning (DQN) and Proximal Policy Optimization (PPO) in a simulated production system. We ...utilize a Petri Net (PN)-based simulation environment, which was previously proposed in related work. The performance of the two algorithms is compared based on several evaluation metrics, including average percentage of correctly assembled and sorted products, average episode length, and percentage of successful episodes. The results show that PPO outperforms DQN in terms of all evaluation metrics. The study highlights the advantages of policy-based algorithms in problems with high-dimensional state and action spaces. The study contributes to the field of deep RL in context of production systems by providing insights into the effectiveness of different algorithms and their suitability for different tasks.
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