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Register a new userReal-time Accelerator Diagnostic Tools for the MAX IV Storage Rings
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In this paper, beam diagnostic and monitoring tools developed by the MAX IV Operations Group are discussed. In particular, new beam position monitoring and accelerator tunes visualization software tools, as well as tools that directly influence the beam quality and stability are introduced. An availability and downtime monitoring application is also presented.
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A long in-plane beam polarization can be a desired feature for spin measurement experiments in storage rings. The spin precession of the particles within a beam can be controlled by means of the frozen spin method and beam bunching via RF cavities, eventually yielding a polarization lifetime of 10--100 seconds. Previous studies have shown that it can be further improved by sextupoles, which correct the second order effects related to the chromaticity of the beam. However, sextupoles can require readjustment after slight changes in ring parameters. This work presents a real-time sextupole tuning method that relies on a feedback algorithm. It adjusts the sextupole strength during storage, targeting a zero average radial spin component. Satisfying this condition results in a longer polarization lifetime. Simulation studies show that roughly determined feedback coefficients in this method work effectively for a wide range of ring parameters, with practical field imperfections and measurement errors taken into account. Alternatively, this technique can be used to optimize sextupole strengths in a test run without intervening the measurement.
Wakefield accelerators are under development in many laboratories worldwide. They bring the promise of a high accelerating gradient, orders of magnitude higher than current machines. The reduction in the overall length of the accelerators will pave the way to a wider use of such machines, for industrial, medical, research, and educational purposes. At the same time, all the equipment must be reduced as well, to keep the dimensions of the machine as small as possible. The two main challenges of the diagnostics for plasma accelerated electron beams are the ability to measure the 6D phase space properties with single shot techniques and the compactness to meet the requirements of a `table-top facility.
Our objective will be to integrate ML into Fermilab accelerator operations and furthermore provide an accessible framework which can also be used by a broad range of other accelerator systems with dynamic tuning needs. We will develop of real-time accelerator control using embedded ML on-chip hardware and fast communication between distributed systems in this proposal. We will demonstrate this technology for the Mu2e experiment by increasing the overall duty factor and uptime of the experiment through two synergistic projects. First, we will use deep reinforcement learning techniques to improve the performance of the regulation loop through guided optimization to provide stable proton beams extracted from the Delivery Ring to the Mu2e experiment. This requires the development of a digital twin of the system to model the accelerator and develop real-time ML algorithms. Second, we will use de-blending techniques to disentangle and classify overlapping beam losses in the Main Injector and Recycler Ring to reduce overall beam downtime in each machine. This ML model will be deployed within a semi-autonomous operational mode. Both applications require processing at the millisecond scale and will share similar ML-in-hardware techniques and beam instrumentation readout technology. A collaboration between Fermilab and Northwestern University will pull together the talents and resources of accelerator physicists, beam instrumentation engineers, embedded system architects, FPGA board design experts, and ML experts to solve complex real-time accelerator controls challenges which will enhance the physics program. More broadly, the framework developed for Accelerator Real-time Edge AI Distributed Systems (READS) can be applied to future projects as the accelerator complex is upgraded for the PIP-II and DUNE era.
We describe a method for precisely regulating the gradient magnet power supply at the Fermilab Booster accelerator complex using a neural network trained via reinforcement learning. We demonstrate preliminary results by training a surrogate machine-learning model on real accelerator data to emulate the Booster environment, and using this surrogate model in turn to train the neural network for its regulation task. We additionally show how the neural networks to be deployed for control purposes may be compiled to execute on field-programmable gate arrays. This capability is important for operational stability in complicated environments such as an accelerator facility.
We report some highlights from the ARIES APEC workshop on ``Storage Rings and Gravitational Waves (SRGW2021), held in virtual space from 2 February to 18 March 2021, and sketch a tentative landscape for using accelerators and associated technologies for the detection or generation of gravitational waves.
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