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The Fermilab Booster is a fast ramping (15Hz) synchrotron which accelerates protons from 400MeV to 8GeV. During commissioning of a transverse digital damper system, it was shown that the damper could provide a measurement of the machine tune throughout the cycle by exciting just 1 of the 84 bunches with minimal impact on the machine operation. The algorithms used to make the measurement have been incorporated into the damper FPGA firmware allowing for real-time tune monitoring of all Booster cycles.
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-l
The Fermilab booster has an intensity upgrade plan called the Proton Improvement plan (PIP). The flux throughput goal is 2E17 protons/hour which is almost double the current operation at 1.1E17 protons/hour. The beam loss in the machine is going to b
The development of magnetic cogging is part of the Fermilab Booster upgrade within the Proton Improvement Plan (PIP). The Booster is going to send 2.25E17 protons/hour which is almost double the present flux, 1.4E17 protons/hour to the Main Injector
Detrimental beam dynamics effects limit performance of high intensity rapid cycling synchrotrons (RCS) such as the 8 GeV proton Fermilab Booster. Here we report the results of comprehensive experimental studies of various beam intensity dependent eff
We propose a model-independent quantity, $L/G$, to characterize non-Gaussian tails in beam profiles observed with the Fermilab Booster Ion Profile Monitor. This quantity can be considered a measure of beam halo in the Booster. We use beam dynamics an