Search Results for author: Kiyomi Seiya

Found 3 papers, 1 papers with code

Beyond PID Controllers: PPO with Neuralized PID Policy for Proton Beam Intensity Control in Mu2e

no code implementations • 28 Dec 2023 • Chenwei Xu, Jerry Yao-Chieh Hu, Aakaash Narayanan, Mattson Thieme, Vladimir Nagaslaev, Mark Austin, Jeremy Arnold, Jose Berlioz, Pierrick Hanlet, Aisha Ibrahim, Dennis Nicklaus, Jovan Mitrevski, Jason Michael St. John, Gauri Pradhan, Andrea Saewert, Kiyomi Seiya, Brian Schupbach, Randy Thurman-Keup, Nhan Tran, Rui Shi, Seda Ogrenci, Alexis Maya-Isabelle Shuping, Kyle Hazelwood, Han Liu

We introduce a novel Proximal Policy Optimization (PPO) algorithm aimed at addressing the challenge of maintaining a uniform proton beam intensity delivery in the Muon to Electron Conversion Experiment (Mu2e) at Fermi National Accelerator Laboratory (Fermilab).

Reinforcement Learning (RL)

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Studies of Beam Intensity Effects in Fermilab Booster Synchrotron

no code implementations • 22 Dec 2020 • Jeff Eldred, Valeri Lebedev, Kiyomi Seiya, Vladimir Shiltsev

Detrimental beam dynamics effects limit performance of high intensity rapid cycling synchrotrons (RCS) such as the 8 GeV proton Fermilab Booster.

Accelerator Physics

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

1 code implementation • 14 Nov 2020 • Jason St. John, Christian Herwig, Diana Kafkes, William A. Pellico, Gabriel N. Perdue, Andres Quintero-Parra, Brian A. Schupbach, Kiyomi Seiya, Nhan Tran, Javier M. Duarte, Yunzhi Huang, Malachi Schram, Rachael Keller

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.

Accelerator Physics

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