Author: Mayes, C.E.
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
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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
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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
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TUPOPT046 Electron Transport for the LCLS-II-HE Low Emittance Injector 1103
 
  • Y.M. Nosochkov, C. Adolphsen, R. Coy, C.E. Mayes, T.O. Raubenheimer, M.D. Woodley
    SLAC, Menlo Park, California, USA
 
  Funding: Work supported by the Department of Energy Contract DE-AC02-76SF00515.
The Low Emittance Injector (LEI) is a recent addition to the LCLS-II High Energy (LCLS-II-HE) Project under design at SLAC National Accelerator Laboratory. It will provide a second beam source capable of producing a low emittance electron beam that increases the XFEL photon energy reach to 20 keV. The LEI will include an SRF electron gun, a buncher system, a 1.3 GHz cryomodule, and a beam transport system with a connection to the LCLS-II beamline and a stand-alone diagnostic line. The LEI transport beamlines and diagnostic are discussed.
 
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOPT046  
About • Received ※ 08 June 2022 — Accepted ※ 17 June 2022 — Issue date ※ 08 July 2022  
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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
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WEPOMS055 Cathode Space Charge in Bmad 2380
 
  • N. Wang
    Cornell University, Ithaca, New York, USA
  • J.A. Crittenden, C.M. Gulliford, G.H. Hoffstaetter, D. Sagan
    Cornell University (CLASSE), Cornell Laboratory for Accelerator-Based Sciences and Education, Ithaca, New York, USA
  • C.E. Mayes
    SLAC, Menlo Park, California, USA
 
  Funding: This project was supported by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy.
We present an implementation of charged particle tracking with the cathode space charge effect included which is now openly available in the Bmad toolkit for charged particle simulations. Adaptive step size control is incorporated to improve the computational efficiency. We demonstrate its capability with a simulation of a DC gun and compare it with the well-established space charge code Impact-T.
 
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-WEPOMS055  
About • Received ※ 08 June 2022 — Revised ※ 13 June 2022 — Accepted ※ 16 June 2022 — Issue date ※ 05 July 2022
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