NAPAC2019 - List of Keywords (network)

Paper	Title	Other Keywords	Page
MOPLM09	High-Power Design of a Cavity Combiner for a 352-MHz Solid State Amplifier System at the Advanced Photon Source	cavity, storage-ring, klystron, interface	113
	G.J. Waldschmidt, D.J. Bromberek, A. Goel, D. Horan, A. Nassiri ANL, Lemont, Illinois, USA
	A cavity combiner has been designed as part of a solid state amplifier system at the Advanced Photon Source with a power requirement of up to 200 kW for the full system. Peak field levels and thermal loading have been optimized to enhance the rf and mechanical perfor-mance of the cavity and to augment its reliability. The combiner consists of 16 rotatable input couplers, a re-duced-field output coupler, and static tuning. The power handling capability of the cavity will be evaluated during a back-feed test where an external klystron source will be used to transmit power through the cavity into loads on each of the input couplers.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-NAPAC2019-MOPLM09
About •	paper received ※ 28 August 2019 paper accepted ※ 04 December 2019 issue date ※ 08 October 2019
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUYBB4	Online Modelling and Optimization of Nonlinear Integrable Systems	lattice, optics, experiment, octupole	318
	N. Kuklev, Y.K. Kim University of Chicago, Chicago, Illinois, USA A. Valishev Fermilab, Batavia, Illinois, USA
	Funding: Work supported by National Science Foundation award PHY-1549132, the Center for Bright Beams. Fermi Research Alliance operates Fermilab under Contract DE-AC02-07CH11359 with the US Dept. of Energy. Nonlinear integrable optics was recently proposed as a design approach to increase the limits on beam brightness and intensity imposed by fast collective instabilities. To study these systems experimentally, a new research electron and proton storage ring, the Integrable Optics Test Accelerator, was constructed and recently commissioned at Fermilab. Beam-based diagnostics and online modelling of nonlinear systems presents unique challenges - in this paper, we report on our efforts to develop optimization methods suited for such lattices. We explore the effectiveness of neural networks as fast online surrogate estimators, and integrate them into a beam-based tuning algorithm. We also develop a method of knob dimensionality reduction and subsequent robust multivariate optimization for maximizing key performance metrics under complicated lattice optics constraints.
	Slides TUYBB4 [5.771 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-NAPAC2019-TUYBB4
About •	paper received ※ 03 September 2019 paper accepted ※ 13 September 2019 issue date ※ 08 October 2019
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPLS07	Helical Transmission Line Test Stand for Non-Relativistic BPM Calibration	impedance, simulation, resonance, linac	463
	C.J. Richard NSCL, East Lansing, Michigan, USA S.M. Lidia FRIB, East Lansing, Michigan, USA
	Measurements of non-relativistic beams by coupling to the fields are affected by the properties of the non-relativistic fields. The authors propose calibrating for these effects with a test stand using a helical line which can propagate pulses at low velocities. Presented are simulations of a helical transmission line for such a test stand which propagates pulses at 0.033c. A description of the helix geometry used to reduce dispersion is given as well as the geometry of the input network.
	Poster TUPLS07 [3.469 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-NAPAC2019-TUPLS07
About •	paper received ※ 27 August 2019 paper accepted ※ 05 September 2019 issue date ※ 08 October 2019
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPLS14	Analyzing Accelerator Operation Data with Neural Networks	injection, operation, storage-ring, booster	487
	F.Y. Wang, X. Huang, Z. Zhang SLAC, Menlo Park, California, USA
	Funding: Work is supported by DOE contract DE-AC02-76SF00515 (SLAC) and DOE contracts 2018-SLAC-100469 and 2018-SLAC-100469ASCR. Accelerator operation history data are used to train neural networks in an attempt to understand the underly-ing causes of performance drifts. In the study, injection efficiency of SPEAR3 [1] over two runs is modelled with a neural network (NN) to map the relationship of the injection efficiency with the injected beam trajectory and environment variables. The NN model can accurately predict the injection performance for the test data. With the model, we discovered that an environment parameter, the ground temperature, has a big impact to the injection performance. The ideal trajectory as a function of the ground temperature can be extracted from the model. The method has the potential for even larger scale application for the discovery of deep connections between machine performance and environment parameters.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-NAPAC2019-TUPLS14
About •	paper received ※ 29 August 2019 paper accepted ※ 06 September 2019 issue date ※ 08 October 2019
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPLH18	NSLS-II Inject Linac RF Control Electronics Upgrade	controls, linac, klystron, operation	516
	H. Ma BNL, Upton, New York, USA
	Funding: US DOE The electron LINAC injector of NSLS-II synchrotron light-source runs both Single-Bunch beam and long Multi-Bunch beam of up to 150 bunches. The key component for achieving this dual injector beam mode support capability is a high-speed rf modulator (or RFM) in the LINAC RF electronics front-end, which performs the necessary rf control and the beam loading compensation of different injection beams. The original LINAC rf electronics front-end successfully supported the machine commissioning and meets the basic needs of the machine operation. The upgrade being pursued is focused on improving the RFM control performance through replacing the current analog implementation in the RFM with a much more capable digital implementation, while still maintaining the necessary control bandwidth that is required for long and short Multi-Bunch beams. A variety of modern COTS rf transmission/reception DSP technology will be incorporated in the new design. The improvement in the reliability of network connection between the RFM’s and their host server is another focus in the upgrade, and the solution includes the adoption of the COTS TCP/IP and other communication protocol offload engines.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-NAPAC2019-TUPLH18
About •	paper received ※ 27 August 2019 paper accepted ※ 15 September 2019 issue date ※ 08 October 2019
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPLE06	Linear and Second Order Map Tracking with Artificial Neural Network	simulation, framework, software, storage-ring	895
	Y.P. Sun ANL, Lemont, Illinois, USA
	Funding: Work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357. In particle accelerators, the tracking simulation is usually performed with symplectic integration, or linear/nonlinear transfer maps. In this paper, it is shown that the linear/nonlinear transfer maps may be represented by an artificial neural network. To solve this multivariate regression problem, both random datasets and structured datasets are explored to train the neural networks. The achieved accuracy will be discussed.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-NAPAC2019-WEPLE06
About •	paper received ※ 30 August 2019 paper accepted ※ 04 September 2019 issue date ※ 08 October 2019
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPLE07	Transfer Matrix Classification with Artificial Neural Network	quadrupole, dipole, framework, software	898
	Y.P. Sun ANL, Lemont, Illinois, USA
	Funding: Work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357. Standard neural network algorithms are developed for classification and regression applications. In this paper, the details of the neural network algorithms are presented, together with several applications. Artificial neural network is trained to classify multi-class transfer matrix of different types of particle accelerator components. It is shown that with a fully-connected feedforward neural network, it is possible to get high accuracy of 99% on training data, validation data and test data.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-NAPAC2019-WEPLE07
About •	paper received ※ 30 August 2019 paper accepted ※ 05 September 2019 issue date ※ 08 October 2019
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Other Keywords

Page

MOPLM09

High-Power Design of a Cavity Combiner for a 352-MHz Solid State Amplifier System at the Advanced Photon Source

cavity, storage-ring, klystron, interface

113

G.J. Waldschmidt, D.J. Bromberek, A. Goel, D. Horan, A. Nassiri
ANL, Lemont, Illinois, USA

A cavity combiner has been designed as part of a solid state amplifier system at the Advanced Photon Source with a power requirement of up to 200 kW for the full system. Peak field levels and thermal loading have been optimized to enhance the rf and mechanical perfor-mance of the cavity and to augment its reliability. The combiner consists of 16 rotatable input couplers, a re-duced-field output coupler, and static tuning. The power handling capability of the cavity will be evaluated during a back-feed test where an external klystron source will be used to transmit power through the cavity into loads on each of the input couplers.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-NAPAC2019-MOPLM09

About •

paper received ※ 28 August 2019 paper accepted ※ 04 December 2019 issue date ※ 08 October 2019

Export •

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

TUYBB4

Online Modelling and Optimization of Nonlinear Integrable Systems

lattice, optics, experiment, octupole

318

N. Kuklev, Y.K. Kim
University of Chicago, Chicago, Illinois, USA
A. Valishev
Fermilab, Batavia, Illinois, USA

Funding: Work supported by National Science Foundation award PHY-1549132, the Center for Bright Beams. Fermi Research Alliance operates Fermilab under Contract DE-AC02-07CH11359 with the US Dept. of Energy.
Nonlinear integrable optics was recently proposed as a design approach to increase the limits on beam brightness and intensity imposed by fast collective instabilities. To study these systems experimentally, a new research electron and proton storage ring, the Integrable Optics Test Accelerator, was constructed and recently commissioned at Fermilab. Beam-based diagnostics and online modelling of nonlinear systems presents unique challenges - in this paper, we report on our efforts to develop optimization methods suited for such lattices. We explore the effectiveness of neural networks as fast online surrogate estimators, and integrate them into a beam-based tuning algorithm. We also develop a method of knob dimensionality reduction and subsequent robust multivariate optimization for maximizing key performance metrics under complicated lattice optics constraints.

Slides TUYBB4 [5.771 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-NAPAC2019-TUYBB4

About •

paper received ※ 03 September 2019 paper accepted ※ 13 September 2019 issue date ※ 08 October 2019

Export •

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

TUPLS07

Helical Transmission Line Test Stand for Non-Relativistic BPM Calibration

impedance, simulation, resonance, linac

463

C.J. Richard
NSCL, East Lansing, Michigan, USA
S.M. Lidia
FRIB, East Lansing, Michigan, USA

Measurements of non-relativistic beams by coupling to the fields are affected by the properties of the non-relativistic fields. The authors propose calibrating for these effects with a test stand using a helical line which can propagate pulses at low velocities. Presented are simulations of a helical transmission line for such a test stand which propagates pulses at 0.033c. A description of the helix geometry used to reduce dispersion is given as well as the geometry of the input network.

Poster TUPLS07 [3.469 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-NAPAC2019-TUPLS07

About •

paper received ※ 27 August 2019 paper accepted ※ 05 September 2019 issue date ※ 08 October 2019

Export •

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

TUPLS14

Analyzing Accelerator Operation Data with Neural Networks

injection, operation, storage-ring, booster

487

F.Y. Wang, X. Huang, Z. Zhang
SLAC, Menlo Park, California, USA

Funding: Work is supported by DOE contract DE-AC02-76SF00515 (SLAC) and DOE contracts 2018-SLAC-100469 and 2018-SLAC-100469ASCR.
Accelerator operation history data are used to train neural networks in an attempt to understand the underly-ing causes of performance drifts. In the study, injection efficiency of SPEAR3 [1] over two runs is modelled with a neural network (NN) to map the relationship of the injection efficiency with the injected beam trajectory and environment variables. The NN model can accurately predict the injection performance for the test data. With the model, we discovered that an environment parameter, the ground temperature, has a big impact to the injection performance. The ideal trajectory as a function of the ground temperature can be extracted from the model. The method has the potential for even larger scale application for the discovery of deep connections between machine performance and environment parameters.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-NAPAC2019-TUPLS14

About •

paper received ※ 29 August 2019 paper accepted ※ 06 September 2019 issue date ※ 08 October 2019

Export •

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

TUPLH18

NSLS-II Inject Linac RF Control Electronics Upgrade

controls, linac, klystron, operation

516

H. Ma
BNL, Upton, New York, USA

Funding: US DOE
The electron LINAC injector of NSLS-II synchrotron light-source runs both Single-Bunch beam and long Multi-Bunch beam of up to 150 bunches. The key component for achieving this dual injector beam mode support capability is a high-speed rf modulator (or RFM) in the LINAC RF electronics front-end, which performs the necessary rf control and the beam loading compensation of different injection beams. The original LINAC rf electronics front-end successfully supported the machine commissioning and meets the basic needs of the machine operation. The upgrade being pursued is focused on improving the RFM control performance through replacing the current analog implementation in the RFM with a much more capable digital implementation, while still maintaining the necessary control bandwidth that is required for long and short Multi-Bunch beams. A variety of modern COTS rf transmission/reception DSP technology will be incorporated in the new design. The improvement in the reliability of network connection between the RFM’s and their host server is another focus in the upgrade, and the solution includes the adoption of the COTS TCP/IP and other communication protocol offload engines.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-NAPAC2019-TUPLH18

About •

paper received ※ 27 August 2019 paper accepted ※ 15 September 2019 issue date ※ 08 October 2019

Export •

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

WEPLE06

Linear and Second Order Map Tracking with Artificial Neural Network

simulation, framework, software, storage-ring

895

Y.P. Sun
ANL, Lemont, Illinois, USA

Funding: Work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357.
In particle accelerators, the tracking simulation is usually performed with symplectic integration, or linear/nonlinear transfer maps. In this paper, it is shown that the linear/nonlinear transfer maps may be represented by an artificial neural network. To solve this multivariate regression problem, both random datasets and structured datasets are explored to train the neural networks. The achieved accuracy will be discussed.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-NAPAC2019-WEPLE06

About •

paper received ※ 30 August 2019 paper accepted ※ 04 September 2019 issue date ※ 08 October 2019

Export •

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

WEPLE07

Transfer Matrix Classification with Artificial Neural Network

quadrupole, dipole, framework, software

898

Y.P. Sun
ANL, Lemont, Illinois, USA

Funding: Work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357.
Standard neural network algorithms are developed for classification and regression applications. In this paper, the details of the neural network algorithms are presented, together with several applications. Artificial neural network is trained to classify multi-class transfer matrix of different types of particle accelerator components. It is shown that with a fully-connected feedforward neural network, it is possible to get high accuracy of 99% on training data, validation data and test data.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-NAPAC2019-WEPLE07

About •

paper received ※ 30 August 2019 paper accepted ※ 05 September 2019 issue date ※ 08 October 2019

Export •

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