IPAC2018 - List of Authors (Fliller, R.P.)

Paper	Title	Page
TUPMF036	Top Off of NSLS-II with Inefficient Injector	1327
	R.P. Fliller, A.A. Derbenev, V.V. Smaluk, X. Yang BNL, Upton, Long Island, New York, USA
	Funding: This manuscript has been authored by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy The NSLS-II is a 3 GeV storage with a full energy injector capable of top off injection. The injector consists of a 200 MeV linac injecting a 3 GeV booster. Recent operational events have caused us to investigate 100 MeV injection into the booster. As the booster was not designed for injection at this low energy, beam loss is observed with this low energy booster injection. This beam loss not only results of overall charge loss from the train, but a change in the overall charge distribution in the bunch train. In this paper we discuss the performance of injecting into the storage ring with the inefficient charge transfer through the injector. The changes to the top off method are discussed, as well as the achieved storage ring current stability and fill pattern.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-TUPMF036
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPMF037	Development of New Operational Mode for NSLS-II Injector: Low Energy 100MeV Linac-to-Booster Injection	1330
	X. Yang, A.A. Derbenev, R.P. Fliller, T.V. Shaftan, V.V. Smaluk BNL, Upton, Long Island, New York, USA
	The NSLS-II injector consists of a 200 MeV linac and a 3 GeV full-energy booster synchrotron. The linac contains five traveling-wave S-band accelerating structures driven by two high-power klystrons, with a third klystron as spare. In the event that the spare klystron is not available, the failure of one klystron will prohibit the linac from injecting into the booster as the energy is too low. Therefore, we wish to develop a new operational mode that the NSLS-II injector can operate with a single klystron providing 100 MeV beam from the linac. A decremented approach with intermediate energies 170 MeV, 150 MeV, etc., takes advantages of pre-calculated booster ramps and beam based online optimization. By lowering the booster injection energy in a small step and online optimizing at each step, we were able to achieve 100 MeV booster injection. 170 MeV operation mode of the NSLS-II injector has been implemented since May 31, 2017, with a similar overall performance compared to the standard 200 MeV operation but fewer klystron trips. 100 MeV single-klystron operation has been successfully demonstrated with 20-30% overall efficiency, which is limited by booster acceptance.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-TUPMF037
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPAF013	Database for the Management of NSLS-II Active Interlock System	1841
	J. Choi, R.P. Fliller, K. Ha, Y. Tian BNL, Upton, Long Island, New York, USA
	Funding: DOE Contract No. DE-SC0012704 NSLS-II is operating the active interlock (AI) system to protect the machine components from the synchrotron radiation from the accidentally mis-steered electron beam. For the systematic management, a relational database is dedicated to the AI system and working as the data provider as well as the archiver. The paper shows how the database is structured and used for the AI system.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-WEPAF013
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPAF022	Application of Machine Learning to Minimize Long Term Drifts in the NSLS-II Linac	1867
	R.P. Fliller, C. Gardner, P. Marino, R.S. Rainer, M. Santana, G.J. Weiner, X. Yang, E. Zeitler BNL, Upton, Long Island, New York, USA
	Funding: This manuscript has been authored by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy Machine Learning has proven itself as a useful technique in a variety of applications from image recognition to playing Go. Artificial Neural Networks have certain advantages when used as a feedforward system, such as the predicted correction relies on a model built from data. This allows for the Artificial Neural Network to compensate for effects that are difficult to model such as low level RF adjustments to compensate for long term drifts. The NSLS-II linac suffers from long terms drifts from a number of sources including thermal drifts and klystron gain variations. These drifts have an effect on the injection efficiency into the booster, and if left unchecked, portions of the bunch train may not be injected into the booster, and the storage ring bunch pattern will ultimately suffer. In this paper, we discuss the application of Artificial Neural Networks to compensate for long term drifts in the NSLS-II linear accelerator. The Artificial Neural Network is implemented in python allowing for rapid development of the network. We discuss the design and training of the network, along with results of using the network in operation.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-WEPAF022
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Top Off of NSLS-II with Inefficient Injector

1327

R.P. Fliller, A.A. Derbenev, V.V. Smaluk, X. Yang
BNL, Upton, Long Island, New York, USA

Funding: This manuscript has been authored by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy
The NSLS-II is a 3 GeV storage with a full energy injector capable of top off injection. The injector consists of a 200 MeV linac injecting a 3 GeV booster. Recent operational events have caused us to investigate 100 MeV injection into the booster. As the booster was not designed for injection at this low energy, beam loss is observed with this low energy booster injection. This beam loss not only results of overall charge loss from the train, but a change in the overall charge distribution in the bunch train. In this paper we discuss the performance of injecting into the storage ring with the inefficient charge transfer through the injector. The changes to the top off method are discussed, as well as the achieved storage ring current stability and fill pattern.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-TUPMF036

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPMF037

Development of New Operational Mode for NSLS-II Injector: Low Energy 100MeV Linac-to-Booster Injection

1330

X. Yang, A.A. Derbenev, R.P. Fliller, T.V. Shaftan, V.V. Smaluk
BNL, Upton, Long Island, New York, USA

The NSLS-II injector consists of a 200 MeV linac and a 3 GeV full-energy booster synchrotron. The linac contains five traveling-wave S-band accelerating structures driven by two high-power klystrons, with a third klystron as spare. In the event that the spare klystron is not available, the failure of one klystron will prohibit the linac from injecting into the booster as the energy is too low. Therefore, we wish to develop a new operational mode that the NSLS-II injector can operate with a single klystron providing 100 MeV beam from the linac. A decremented approach with intermediate energies 170 MeV, 150 MeV, etc., takes advantages of pre-calculated booster ramps and beam based online optimization. By lowering the booster injection energy in a small step and online optimizing at each step, we were able to achieve 100 MeV booster injection. 170 MeV operation mode of the NSLS-II injector has been implemented since May 31, 2017, with a similar overall performance compared to the standard 200 MeV operation but fewer klystron trips. 100 MeV single-klystron operation has been successfully demonstrated with 20-30% overall efficiency, which is limited by booster acceptance.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-TUPMF037

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPAF013

Database for the Management of NSLS-II Active Interlock System

1841

J. Choi, R.P. Fliller, K. Ha, Y. Tian
BNL, Upton, Long Island, New York, USA

Funding: DOE Contract No. DE-SC0012704
NSLS-II is operating the active interlock (AI) system to protect the machine components from the synchrotron radiation from the accidentally mis-steered electron beam. For the systematic management, a relational database is dedicated to the AI system and working as the data provider as well as the archiver. The paper shows how the database is structured and used for the AI system.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-WEPAF013

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPAF022

Application of Machine Learning to Minimize Long Term Drifts in the NSLS-II Linac

1867

R.P. Fliller, C. Gardner, P. Marino, R.S. Rainer, M. Santana, G.J. Weiner, X. Yang, E. Zeitler
BNL, Upton, Long Island, New York, USA

Funding: This manuscript has been authored by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy
Machine Learning has proven itself as a useful technique in a variety of applications from image recognition to playing Go. Artificial Neural Networks have certain advantages when used as a feedforward system, such as the predicted correction relies on a model built from data. This allows for the Artificial Neural Network to compensate for effects that are difficult to model such as low level RF adjustments to compensate for long term drifts. The NSLS-II linac suffers from long terms drifts from a number of sources including thermal drifts and klystron gain variations. These drifts have an effect on the injection efficiency into the booster, and if left unchecked, portions of the bunch train may not be injected into the booster, and the storage ring bunch pattern will ultimately suffer. In this paper, we discuss the application of Artificial Neural Networks to compensate for long term drifts in the NSLS-II linear accelerator. The Artificial Neural Network is implemented in python allowing for rapid development of the network. We discuss the design and training of the network, along with results of using the network in operation.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-WEPAF022

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)