Author: Sun, Y.
Paper Title Page
TUPAB286 Experience with On-line Optimizers for APS Linac Front End Optimization 2151
 
  • H. Shang, M. Borland, X. Huang, Y. Sun
    ANL, Lemont, Illinois, USA
  • M. Song, Z. Zhang
    SLAC, Menlo Park, California, USA
 
  Funding: * Work supported by U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357 and BES R&D project FWP 2020-ANL-34573
While the APS linac lattice is set up using a model developed with ELEGANT, the thermionic RF gun front end beam dynamics has been difficult to model. One of the issues is that beam properties from the thermionic gun can vary from time to time. As a result, linac front end beam tuning is required to establish good matching and maximize the charge transported through the linac. We have been using a traditional simplex optimizer to find the best settings for the gun front end magnets and steering magnets. However, it takes a long time and requires some fair initial conditions. Therefore, we imported other on-line optimizers, such as robust conjugate direction search (RCDS) which is a classic optimizer as simplex, multi-objective particle swarm (MOPSO), and multi-generation gaussian process optimizer (MG-GPO) which is based on machine learning technique. In this paper we report our experience with these on-line optimizers for maximum bunch charge transportation efficiency through the linac.
 
poster icon Poster TUPAB286 [2.964 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB286  
About • paper received ※ 12 May 2021       paper accepted ※ 08 July 2021       issue date ※ 29 August 2021  
Export • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
 
TUPAB287 Application of Artificial Neural Network in the APS Linac Bunch Charge Transmission Efficiency 2155
 
  • H. Shang, R. Maulik, Y. Sun
    ANL, Lemont, Illinois, USA
  • T. Xu
    Northern Illinois University, DeKalb, Illinois, USA
 
  Funding: * Work supported by U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357.
In recent years there has been a rapid growth in machine learning (ML) and artificial intelligence (AI) applications in accelerators. As the scale of complexity and sophistication of modern accelerators grows, the difficulties in modeling the machine increase greatly in order to include all the interacting subsystems and to consider the limitation of various diagnostics to benchmark against measurements. Tools based on ML can help substantially in revealing correlations of machine condition and beam parameters that are not easily discovered using traditional physics model-based simulations, reducing machine tuning up time etc among the many possible applications. While at APS we have many excellent tools for the optimization, diagnostics, and controls of the accelerators, we do not yet have ML-based tools established. It is our desire to test ML in our machine operation, optimization, and controls. In this paper, we introduce the application of neural networks to the APS linac bunch charge transmission efficiency.
 
poster icon Poster TUPAB287 [0.781 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB287  
About • paper received ※ 12 May 2021       paper accepted ※ 16 June 2021       issue date ※ 29 August 2021  
Export • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
 
TUPAB290 Demonstration of Machine Learning Front-End Optimization of the Advanced Photon Source Linac 2163
 
  • A. Hanuka, J.P. Duris
    SLAC, Menlo Park, California, USA
  • H. Shang, Y. Sun
    ANL, Lemont, Illinois, USA
 
  The electron beam for the Advanced Photon Source (APS) at Argonne National Laboratory is generated from a thermionic RF gun and accelerated by an S-band linear accelerator – the APS linac. While the APS linac lattice is set up using a model developed with ELEGANT, the thermionic RF gun front-end beam dynamics have been difficult to model. One of the issues is that beam properties from thermionic guns can vary. As a result, linac front-end beam tuning is required to establish good matching and maximize the charge transported through the linac. A traditional Nelder-Mead simplex optimizer has been used to find the best settings for the sixteen quadrupoles and steering magnets. However, it takes a long time and requires some fair initial conditions. The Gaussian Process (GP) optimizer does not have the initial condition limitation and runs several times faster. In this paper, we report our data collection and analysis for the training of the GP hyperparameters and discuss the application of GP optimizer on the APS linac front-end optimization for maximum bunch charge transportation efficiency through the linac.  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB290  
About • paper received ※ 09 May 2021       paper accepted ※ 28 July 2021       issue date ※ 27 August 2021  
Export • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
 
THPAB082 Recent Operational Experience with Thermionic RF Guns at the APS 3959
 
  • Y. Sun, M. Borland, G.I. Fystro, X. Huang, H. Shang
    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
The electron beam at the Argonne Advanced Photon Source (APS) is generated from an S-band thermionic RF gun. There are two locations at the frontend of the linac where thermionic RF guns are installed – RG1 and RG2. Three so-called generation-III guns are available, two are installed at RG1 and RG2, one is a spare. In recent years, these guns are showing signs of aging after over a couple of decades of operations. RF trips started to occur, and we had to reduce the nominal operating rf power to alleviate the problem. In addition, beam generated by RG1 suffers from low transportation efficiency from the gun to the linac, and beam trajectory is unstable which results in charge instabilities. Recently, APS obtained a new type of prototype gun and it was beam commissioned in the linac. In this paper, we report our operational experience with these thermionic rf guns including thermionic-cathode beam extraction, gun front-end optimization for maximum charge transmission through the linac, linac lattice setup to match beam for injection into the Particle Accumulator Ring (PAR) and optimization for maximum PAR injection efficiency.
 
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB082  
About • paper received ※ 19 May 2021       paper accepted ※ 28 July 2021       issue date ※ 26 August 2021  
Export • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
 
THPAB083 Measurement of the Longitudinal Phase-Space of the APS Photo-Injector Beam 3963
 
  • Y. 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.
An S-band photo-cathode RF gun (PCG) exists at the front of the linac. The high-brightness photoinjector beam is accelerated by the linac and and can be used for accelerator technology and beam physics R&D experiments in the Linac Extension Area (LEA). For some applications, the beam needs to be compressed by a magnetic bunch compressor in the middle of the linac. An S-band transverse-mode cavity (Tcav) is available at the end of the linac for beam longitudinal phase-space diagnostics. Beam commissioning experience of the Tcav is reported in this paper. The cavity rf conditioning and calibration was performed. There is a horizontally bending dipole magnet downstream of the Tcav, which kicks beam in the vertical plane. Beam image on a YAG screen downstream of the Tcav and dipole magnet contains the single-shot information of the longitudinal phase-space of the photo-injector beam. The first measurements of the longitudinal phase-space of the compressed and non-compressed photoinjector beam are discussed. Improvements of the measurement resolution are planned.
 
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB083  
About • paper received ※ 25 May 2021       paper accepted ※ 12 July 2021       issue date ※ 21 August 2021  
Export • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)