IPAC2022 - List of Authors (Roussel, R.J.)

Paper	Title	Page
TUPOST056	Multi-Objective Bayesian Optimization at SLAC MeV-UED	995
	F. Ji, A.L. Edelen, R.J. England, P.L. Kramer, D. Luo, C.E. Mayes, M.P. Minitti, S.A. Miskovich, M. Mo, A.H. Reid, R.J. Roussel, X. Shen, X.J. Wang, S.P. Weathersby SLAC, Menlo Park, California, USA
	SLAC MeV-UED, part of the LCLS user facility, is a powerful ’electron camera’ for the study of ultrafast molecular structural dynamics and the coupling of electronic and atomic motions in a variety of material and chemical systems. The growing demand of scientific applications calls for rapid switching between different beamline configurations for delivering electron beams meeting specific user run requirements, necessitating fast online tuning strategies to reduce set up time. Here, we utilize multi-objective Bayesian optimization(MOBO) for fast searching the parameter space efficiently in a serialized manner, and mapping out the Pareto Front which gives the trade-offs between key beam parameters, i.e., spot size, q-resolution, pulse length, pulse charge, etc. Algorithm, model deployment and first test results will be presented.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOST056
About •	Received ※ 08 June 2022 — Revised ※ 14 June 2022 — Accepted ※ 16 June 2022 — Issue date ※ 09 July 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPOST058	Badger: The Missing Optimizer in ACR	999
	Z. Zhang, A.L. Edelen, J.R. Garrahan, C.E. Mayes, S.A. Miskovich, D.F. Ratner, R.J. Roussel, J. Shtalenkova SLAC, Menlo Park, California, USA M. Böse, S. Tomin DESY, Hamburg, Germany Y. Hidaka, G.M. Wang BNL, Upton, New York, USA
	Badger is an optimizer specifically designed for Accelerator Control Room (ACR). It’s the spiritual successor of Ocelot optimizer. Badger abstracts an optimization run as an optimization algorithm interacts with an environment, by following some pre-defined rules. The environment is controlled by the algorithm and tunes/observes the control system/machine through an interface, while the users control/monitor the optimization flow through a graphical user interface (GUI) or a command line interface (CLI). This paper would introduce the design principles and applications of Badger.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOST058
About •	Received ※ 08 June 2022 — Revised ※ 10 June 2022 — Accepted ※ 15 June 2022 — Issue date ※ 17 June 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPOMS013	Neural Network Solver for Coherent Synchrotron Radiation Wakefield Calculations in Accelerator-Based Charged Particle Beams	2261
	A.L. Edelen, C. Emma, C.E. Mayes, R.J. Roussel SLAC, Menlo Park, California, USA
	Particle accelerators support a wide array of scientific, industrial, and medical applications. To meet the needs of these applications, accelerator physicists rely heavily on detailed simulations of the complicated particle beam dynamics through the accelerator. One of the most computationally expensive and difficult-to-model effects is the impact of Coherent Synchrotron Radiation (CSR). CSR is one of the major drivers of growth in the beam emittance, which is a key metric of beam quality that is critical in many applications. The CSR wakefield is very computationally intensive to compute with traditional electromagnetic solvers, and this is a major limitation in accurately simulating accelerators. Here, we demonstrate a new approach for the CSR wakefield computation using a neural network solver structured in a way that is readily generalizable to new setups. We validate its performance by adding it to a standard beam tracking test problem and show a ten-fold speedup along with high accuracy.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-WEPOMS013
About •	Received ※ 10 June 2022 — Revised ※ 16 June 2022 — Accepted ※ 17 June 2022 — Issue date ※ 03 July 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Multi-Objective Bayesian Optimization at SLAC MeV-UED

995

F. Ji, A.L. Edelen, R.J. England, P.L. Kramer, D. Luo, C.E. Mayes, M.P. Minitti, S.A. Miskovich, M. Mo, A.H. Reid, R.J. Roussel, X. Shen, X.J. Wang, S.P. Weathersby
SLAC, Menlo Park, California, USA

SLAC MeV-UED, part of the LCLS user facility, is a powerful ’electron camera’ for the study of ultrafast molecular structural dynamics and the coupling of electronic and atomic motions in a variety of material and chemical systems. The growing demand of scientific applications calls for rapid switching between different beamline configurations for delivering electron beams meeting specific user run requirements, necessitating fast online tuning strategies to reduce set up time. Here, we utilize multi-objective Bayesian optimization(MOBO) for fast searching the parameter space efficiently in a serialized manner, and mapping out the Pareto Front which gives the trade-offs between key beam parameters, i.e., spot size, q-resolution, pulse length, pulse charge, etc. Algorithm, model deployment and first test results will be presented.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOST056

About •

Received ※ 08 June 2022 — Revised ※ 14 June 2022 — Accepted ※ 16 June 2022 — Issue date ※ 09 July 2022

Cite •

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

TUPOST058

Badger: The Missing Optimizer in ACR

999

Z. Zhang, A.L. Edelen, J.R. Garrahan, C.E. Mayes, S.A. Miskovich, D.F. Ratner, R.J. Roussel, J. Shtalenkova
SLAC, Menlo Park, California, USA
M. Böse, S. Tomin
DESY, Hamburg, Germany
Y. Hidaka, G.M. Wang
BNL, Upton, New York, USA

Badger is an optimizer specifically designed for Accelerator Control Room (ACR). It’s the spiritual successor of Ocelot optimizer. Badger abstracts an optimization run as an optimization algorithm interacts with an environment, by following some pre-defined rules. The environment is controlled by the algorithm and tunes/observes the control system/machine through an interface, while the users control/monitor the optimization flow through a graphical user interface (GUI) or a command line interface (CLI). This paper would introduce the design principles and applications of Badger.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOST058

About •

Received ※ 08 June 2022 — Revised ※ 10 June 2022 — Accepted ※ 15 June 2022 — Issue date ※ 17 June 2022

Cite •

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

WEPOMS013

Neural Network Solver for Coherent Synchrotron Radiation Wakefield Calculations in Accelerator-Based Charged Particle Beams

2261

A.L. Edelen, C. Emma, C.E. Mayes, R.J. Roussel
SLAC, Menlo Park, California, USA

Particle accelerators support a wide array of scientific, industrial, and medical applications. To meet the needs of these applications, accelerator physicists rely heavily on detailed simulations of the complicated particle beam dynamics through the accelerator. One of the most computationally expensive and difficult-to-model effects is the impact of Coherent Synchrotron Radiation (CSR). CSR is one of the major drivers of growth in the beam emittance, which is a key metric of beam quality that is critical in many applications. The CSR wakefield is very computationally intensive to compute with traditional electromagnetic solvers, and this is a major limitation in accurately simulating accelerators. Here, we demonstrate a new approach for the CSR wakefield computation using a neural network solver structured in a way that is readily generalizable to new setups. We validate its performance by adding it to a standard beam tracking test problem and show a ten-fold speedup along with high accuracy.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-WEPOMS013

About •

Received ※ 10 June 2022 — Revised ※ 16 June 2022 — Accepted ※ 17 June 2022 — Issue date ※ 03 July 2022

Cite •

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