6 research outputs found

    Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster

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    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.Comment: 16 pages, 10 figures. Submitted to Physical Review Accelerators and Beams. For associated dataset and data sheet see http://doi.org/10.5281/zenodo.408898

    Fermilab main injector: High intensity operation and beam loss control

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    From 2005 through 2012, the Fermilab Main Injector provided intense beams of 120 GeV protons to produce neutrino beams and antiprotons. Hardware improvements in conjunction with improved diagnostics allowed the system to reach sustained operation at 400 kW beam power. Transmission was very high except for beam lost at or near the 8 GeV injection energy where 95% beam transmission results in about 1.5 kW of beam loss. By minimizing and localizing loss, residual radiation levels fell while beam power was doubled. Lost beam was directed to either the collimation system or to the beam abort. Critical apertures were increased while improved instrumentation allowed optimal use of available apertures. We will summarize the improvements required to achieve high intensity, the impact of various loss control tools and the status and trends in residual radiation in the Main Injector
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