IPAC2022 - List of Authors (Miskovich, S.A.)

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)

TUPOST059	PyEmittance: A General Python Package for Particle Beam Emittance Measurements with Adaptive Quadrupole Scans	1003
	S.A. Miskovich, A.L. Edelen, C.E. Mayes SLAC, Menlo Park, California, USA
	The emittance of a particle beam is a critically important parameter for many particle accelerator applications. Its measurements guide the initial tuning of an accelerator and are typically done using quadrupole or wire scans. Quadrupole scans are time-intensive, and it can be difficult to determine scan values that provide a good emittance measurement. To address this issue, we describe an adaptive quadrupole scan method that automates the determination of the scan range. With a given initial set of scanning values, our method adapts the range to capture the waist of the beam, and returns the Twiss parameters and a measure of the beam matching at the measurement screen. With the added capability to repeat beam size measurements when needed, this method provides a reliable measurement of the emittance even with sub-optimal initial conditions. To efficiently integrate these measurements into Python-based machine learning optimizations, the method was developed into a Python package, PyEmittance, at the SLAC National Accelerator Laboratory. We present the experimental tests of PyEmittance as performed at the Linac Coherent Light Source (LCLS) and the Facility for Advanced Accelerator Experimental Test (FACET-II).
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOST059
About •	Received ※ 08 June 2022 — Revised ※ 13 June 2022 — Accepted ※ 15 June 2022 — Issue date ※ 17 June 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)

TUPOST059

PyEmittance: A General Python Package for Particle Beam Emittance Measurements with Adaptive Quadrupole Scans

1003

S.A. Miskovich, A.L. Edelen, C.E. Mayes
SLAC, Menlo Park, California, USA

The emittance of a particle beam is a critically important parameter for many particle accelerator applications. Its measurements guide the initial tuning of an accelerator and are typically done using quadrupole or wire scans. Quadrupole scans are time-intensive, and it can be difficult to determine scan values that provide a good emittance measurement. To address this issue, we describe an adaptive quadrupole scan method that automates the determination of the scan range. With a given initial set of scanning values, our method adapts the range to capture the waist of the beam, and returns the Twiss parameters and a measure of the beam matching at the measurement screen. With the added capability to repeat beam size measurements when needed, this method provides a reliable measurement of the emittance even with sub-optimal initial conditions. To efficiently integrate these measurements into Python-based machine learning optimizations, the method was developed into a Python package, PyEmittance, at the SLAC National Accelerator Laboratory. We present the experimental tests of PyEmittance as performed at the Linac Coherent Light Source (LCLS) and the Facility for Advanced Accelerator Experimental Test (FACET-II).

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOST059

About •

Received ※ 08 June 2022 — Revised ※ 13 June 2022 — Accepted ※ 15 June 2022 — Issue date ※ 17 June 2022

Cite •

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