Predictive ion source control using artificial neural network for RFT-30 cyclotron

Kong, Young Bae; Hur, Min Goo; Lee, Eun Je; Park, Jeong Hoon; Park, Yong Dae; Yang, Seung Dae

doi:10.1016/j.nima.2015.09.095

Cited by 15 publications

(8 citation statements)

References 9 publications

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“…• Machine learning (ML) branches from statistics and regression. ML-based tools, such as neural networks (NN), can be trained to automatically tune and control large complex systems such as particle accelerators [37][38][39][40]. ML tools are being developed to provide surrogate models to create diagnostics [41].…”

Section: Advanced Control Methodsmentioning

confidence: 99%

Advanced Control Methods for Particle Accelerators (ACM4PA) 2019

Scheinker

Emma

Edelen

et al. 2019

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Los Alamos is currently developing novel particle accelerator controls and diagnostics algorithms to enable higher quality beams with lower beam losses than is currently possible. The purpose of this workshop was to consider tuning and optimization challenges of a wide range of particle accelerators including linear proton accelerators such as the Los Alamos Neutron Science Center (LANSCE), rings such as the Advanced Photon Source (APS) synchrotron, free electron lasers (FEL) such as the Linac Coherent Light Source (LCLS) and LCLS-II, the European X-ray Free Electron Laser (EuXFEL), the Swiss FEL, and the planned MaRIE FEL, and plasma wake-field accelerators such as FACET, FACET-II, and AWAKE at CERN. One major challenge is an the ability to quickly create very high quality, extremely intense, custom current and energy profile beams while working with limited real time non-invasive diagnostics and utilizing time-varying uncertain initial beam distributions and accelerator components. Currently, a few individual accelerator labs have been developing and applying their own diagnostics tools and custom control and ML algorithms for automated machine tuning and optimization. The goal of this workshop was to bring together a group of accelerator physicists and accelerator related control and ML experts in order to define which controls and diagnostics would be most useful for existing and future accelerators and to create a plan for developing a new family of algorithms that can be shared and maintained by the community. Organizing CommitteeAlexander Scheinker LANL

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Section: Advanced Control Methodsmentioning

confidence: 99%

Advanced Control Methods for Particle Accelerators (ACM4PA) 2019

Scheinker

Emma

Edelen

et al. 2019

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“…Sistem sumber ion yang digunakan pada Siklotron DECY 13 pada saat ini terdiri dari dua buah sub sistem di dalamnya, yaitu katup untuk mengendalikan aliran gas hidrogen dan motor untuk pengerak posisi dari batang sumber ion [2]. Semua kendali masih menggunakan kendali manual yaitu kran valve yang diatur dengan tangan dan gerakan motor posisi dengan tombol on/off [3].…”

Section: Pendahuluanunclassified

“…Tetapi arus berkas yang dihasilkan belum stabil dan tidak linier dalam waktu yang lama. Ketidak stabilan arus berkas yang terjaadi antara lain adalah arus katoda dari catu daya katoda yang tidak stabil, catu daya anoda/penarik berkas yang tidak stabil, tingkat kevakuman pada chamber yang turun, posisi head sumber ion yang tidak pas pada central region dan lain sebagainya [3]. Salah satu dari sekian faktor yang mempengaruhi arus berkas adalah dari aliran gas hidrogen yang dikeluarkan pada tabung gas tidak stabil atau konstan dan cenderung turun selama operasi sehingga ruang vakum berubah tingkat kevakumannya.…”

Section: Pendahuluanunclassified

Perancangan Dan Identifikasi Kendali Pid Pada Aliran Hidrogen Sumber Ion Siklotron Decy 13

Permana¹,

Effendy²,

Wardana³

2021

gnd

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“…Recently, powerful machine learning (ML) techniques have been studied for various particle accelerator applications. ML-based tools, such as neural networks (NN), can be trained to automatically tune and control large complex systems such as particle accelerators [45][46][47][48]. ML tools are being developed to provide fast and accurate surrogate models to create diagnostics that enable feedback control and tuning of accelerators [49].…”

Section: B Machine Learningmentioning

confidence: 99%

Model-independent tuning for maximizing free electron laser pulse energy

Scheinker

Bohler

Tomin

et al. 2019

Phys. Rev. Accel. Beams

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The output power of a free electron laser (FEL) has extremely high variance even when all FEL parameter set points are held constant because of the stochastic nature of the self-amplified spontaneous emission (SASE) FEL process, drift of thousands of coupled parameters, such as thermal drifts, and uncertainty and time variation of the electron distribution coming off of the photo cathode and entering the accelerator. In this work, we demonstrate the application of automatic, model-independent feedback for the maximization of average pulse energy of the light produced by free electron lasers. We present experimental results from both the European x-ray free electron laser at DESY and from the Linac Coherent Light Source at SLAC. We demonstrate application of the technique on rf systems for automatically adjusting the longitudinal phase space of the beam, for adjusting the phase shifter gaps between the undulators, and for adjusting steering magnets between undulator sections to maximize the FEL output power. We show that we can tune up to 105 components simultaneously based only on noisy average bunch energy measurements.

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Predictive ion source control using artificial neural network for RFT-30 cyclotron

Cited by 15 publications

References 9 publications

Advanced Control Methods for Particle Accelerators (ACM4PA) 2019

Advanced Control Methods for Particle Accelerators (ACM4PA) 2019

Perancangan Dan Identifikasi Kendali Pid Pada Aliran Hidrogen Sumber Ion Siklotron Decy 13

Model-independent tuning for maximizing free electron laser pulse energy

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