Journal of Nuclear Science, Engineering and Technology (JONSAT)
In cooperation with the Iranian Nuclear Society

Electron beam control in the storage ring of the ELETTRA 2.0 accelerator using machine learning

Document Type : Research Paper

Authors

M.S. Yazdani 1
S.F. Masoudi 1
J. ًRahighi 2
S. Dastan 3

1 Department of Physics, K.N. Toosi University of Technology, Postal Code: 158754416, Tehran – Iran

2 School of Particles and Accelerators, Institute for Research in Fundamental Sciences (IPM), PO Box: 19395-5746, Tehran, Iran

3 Elettra -Sincrotrone Trieste, Trieste - Italy

https://doi.org/10.24200/nst.2024.1598.2034
Abstract
In recent years, the advancement of electron synchrotron light sources has been crucial for synchrotron radiation users. Improving electron beam qualities, such as stability and intensity, has been key to generating high-brightness photon beams. To achieve a brighter photon beam, electron synchrotron accelerators have been undergoing significant design changes. A critical aspect of their design is precise control of particle trajectories and correction of errors to achieve a stable electron beam and, consequently, a high-intensity photon beam. One interesting approach in this field is the use of neural networks to correct the electron beam position in the storage ring, a process known as beam control. In this study, a convolutional neural network model has been developed for the first time to control the ELETTRA 2.0 storage ring, a synchrotron light source in Italy. The performance of this model, based on machine learning techniques, is approximately 6% better than the ISVD method, while also demonstrating high robustness to real-world data.

Highlights

  1. Leemann S.C, Liu S, Hexemer A, Marcus M.A, Melton C.N, Nishimura H, Sun C. Demonstration of Machine Learning-Based Model-Independent Stabilization of Source Properties in Synchrotron Light Sources. Phys Rev Lett. 2019 Nov 6;123(19):194801.

 

  1. Bartolini R. Storage Ring Design for Synchrotron Radiation Sources. In: Jaeschke EJ, Khan S, Schneider JR, Hastings JB, editors. Synchrotron Light Sources and Free-Electron Lasers [Internet]. Cham: Springer International Publishing; 2016 [cited 2024 Jan 16]. 253–302. Available from: https://link.springer.com/10.1007/978-3-319-14394-1_7.

 

  1. Shin S. New era of synchrotron radiation: fourth-generation storage ring. AAPPS Bull. 2021 Dec;31(1):21.

 

  1. Schirmer D. Intelligent Controls for the Electron Storage Ring DELTA. Proceedings of the 9th Int Particle Accelerator Conf. 2018;IPAC2018:4 pages, 1.097 MB.

 

  1. Koetter S, Riemann B, Isbarn B.D, Sommer M, Weis T. Evaluation of a Cone-Program Based Approach to Orbit Correction at the Electron Storage Ring Delta. Verhandlungen der Deutschen Physikalischen Gesellschaft. 2018;(Wuerzburg2018issue):1.

 

  1. Schirmer D. A machine learning approach to electron orbit control at the 1.5 GeV synchrotron light source DELTA. J Phys: Conf Ser. 2023 Jan 1;2420(1):012069.

 

  1. Brown K, Binello S, D’Ottavio T, Dyer P, Nemesure S, Thomas D. Experience with Machine Learning in Accelerator Controls. Proceedings of the 16th Int Conf on Accelerator and Large Experimental Control Systems. 2018;ICALEPCS2017:7 pages, 0.935 MB.

 

  1. Edelen A.L, Biedron S.G, Chase B.E, Edstrom D, Milton S.V, Stabile P. Neural Networks for Modeling and Control of Particle Accelerators. IEEE Trans Nucl Sci. 2016 Apr;63(2):878–97.

 

  1. Vera Ramirez L, Hartmann G, Mertens T, Müller R, Viefhaus J. Adding Machine Learning to the Analysis and Optimization Toolsets at the Light Source BESSY II. Proceedings of the 17th International Conference on Accelerator and Large Experimental Physics Control Systems. 2020;ICALEPCS2019:7 pages, 2.836 MB.

 

  1. Hanuka A, Huang X, Shtalenkova J, Kennedy D, Edelen A, Zhang Z, Lalchand V.R, Ratner D, Duris J. Physics model-informed Gaussian process for online optimization of particle accelerators. Phys Rev Accel Beams. 2021 Jul 8;24(7):072802.

 

  1. Karantzoulis E, Carniel A, Castronovo D, Di Mitri S, Diviacco B, Krecic S. Elettra and Elettra 2.0. Proceedings of the 12th International Particle Accelerator Conference. 2021;IPAC2021:3 pages, 0.511 MB.

 

  1. Karantzoulis E, Barletta W. Aspects of the Elettra 2.0 design. Nucl. Instrum. Methods Phys. Res. A, 2019 May;927:70–80.

 

  1. Karantzoulis E, Di Mitri S, Barbo F, Barletta W, Bassanese S, Bracco R, Design strategies and technology of Elettra 2.0 for a versatile offer to the user community. Nucl. Instrum. Methods Phys. Res. A, 2024 Mar;1060:169007.

 

  1. Baird S. Accelerators for pedestrians [Internet]. CERN. Geneva. AB Department; 2007 Feb p. 155. (Accelerators and Storage Rings). Report No.: AB-Note-2007-014; CERN-AB-Note-2007-014; PS-OP-Note-95-17-Rev-2; CERN-PS-OP-Note-95-17-Rev-2. Available from: https://cds.cern.ch/record/ 1017689/files/ab-note-2007-014.pdf.

 

  1. Chung Y, Decker G, Evans K. Closed orbit correction using singular value decomposition of the response matrix. In: Proceedings of International Conference on Particle Accelerators. 1993;3:2263–5.

 

  1. Borland M. ELEGANT: A flexible SDDS-compliant code for accelerator simulation [Internet]. 2000 Aug [cited 2024 Feb 3] LS-287, 761286. Report No.: LS-287, 761286. Available from: http://www.osti.gov/servlets/purl/761286/.

 

  1. PART THREE: Machine and Infrastructure [Internet]. Elettra-Sincrotrone Trieste; p. 469. Available from: https://www.elettra.eu/images/ Documents/ELETTRA%20Machine/Elettra2/TDR-Machine-Infrastructures-Final-compresso.pdf.

 

  1. Benjamin C, Golnaraghi F. Automatic control systems. John Wiley and Sons. 2010.

 

  1. Ogata K. Modern control engineering. Pearson, 5th edition. 2010.

 

  1. Limon D, Calliess J, Maciejowski J.M. Learning-based Nonlinear Model Predictive Control. IFAC-PapersOnLine. 2017;50(1):7769–76.

 

  1. Abiodun O.I, Jantan A, Omolara A.E, Dada K.V, Mohamed N.A, Arshad H. State-of-the-art in artificial neural network applications: A survey. Heliyon. 2018 Nov;4(11):e00938.

 

  1. Emami S.A, Castaldi P, Banazadeh A. Neural network-based flight control systems: Present and future. Annual Reviews in Control. 2022;53:97–137.

 

  1. Li Z, Yang W, Peng S, Liu F. A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects. CoRR [Internet]. 2020;abs/2004.02806. Available from: https://arxiv.org/abs/2004.02806.

 

  1. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015 May 28;521(7553):436–44.

 

  1. Kiranyaz S, Avci O, Abdeljaber O, Ince T, Gabbouj M, Inman D.J. 1D convolutional neural networks and applications: A survey. Mechanical Systems and Signal Processing. 2021 Apr;151:107398.

 

  1. Smola A.J, Schölkopf B. A tutorial on support vector regression. Statistics and computing. 2004;14:199–222.

 

  1. Borchani H, Varando G, Bielza C, Larranaga P. A survey on multi-output regression. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2015;5(5):216–33.

Keywords

Machine learning
Convolutional neural network
Artificial neural controller
Orbit control
Elettra 2.0 storage ring
  1. Leemann S.C, Liu S, Hexemer A, Marcus M.A, Melton C.N, Nishimura H, Sun C. Demonstration of Machine Learning-Based Model-Independent Stabilization of Source Properties in Synchrotron Light Sources. Phys Rev Lett. 2019 Nov 6;123(19):194801.

 

  1. Bartolini R. Storage Ring Design for Synchrotron Radiation Sources. In: Jaeschke EJ, Khan S, Schneider JR, Hastings JB, editors. Synchrotron Light Sources and Free-Electron Lasers [Internet]. Cham: Springer International Publishing; 2016 [cited 2024 Jan 16]. 253–302. Available from: https://link.springer.com/10.1007/978-3-319-14394-1_7.

 

  1. Shin S. New era of synchrotron radiation: fourth-generation storage ring. AAPPS Bull. 2021 Dec;31(1):21.

 

  1. Schirmer D. Intelligent Controls for the Electron Storage Ring DELTA. Proceedings of the 9th Int Particle Accelerator Conf. 2018;IPAC2018:4 pages, 1.097 MB.

 

  1. Koetter S, Riemann B, Isbarn B.D, Sommer M, Weis T. Evaluation of a Cone-Program Based Approach to Orbit Correction at the Electron Storage Ring Delta. Verhandlungen der Deutschen Physikalischen Gesellschaft. 2018;(Wuerzburg2018issue):1.

 

  1. Schirmer D. A machine learning approach to electron orbit control at the 1.5 GeV synchrotron light source DELTA. J Phys: Conf Ser. 2023 Jan 1;2420(1):012069.

 

  1. Brown K, Binello S, D’Ottavio T, Dyer P, Nemesure S, Thomas D. Experience with Machine Learning in Accelerator Controls. Proceedings of the 16th Int Conf on Accelerator and Large Experimental Control Systems. 2018;ICALEPCS2017:7 pages, 0.935 MB.

 

  1. Edelen A.L, Biedron S.G, Chase B.E, Edstrom D, Milton S.V, Stabile P. Neural Networks for Modeling and Control of Particle Accelerators. IEEE Trans Nucl Sci. 2016 Apr;63(2):878–97.

 

  1. Vera Ramirez L, Hartmann G, Mertens T, Müller R, Viefhaus J. Adding Machine Learning to the Analysis and Optimization Toolsets at the Light Source BESSY II. Proceedings of the 17th International Conference on Accelerator and Large Experimental Physics Control Systems. 2020;ICALEPCS2019:7 pages, 2.836 MB.

 

  1. Hanuka A, Huang X, Shtalenkova J, Kennedy D, Edelen A, Zhang Z, Lalchand V.R, Ratner D, Duris J. Physics model-informed Gaussian process for online optimization of particle accelerators. Phys Rev Accel Beams. 2021 Jul 8;24(7):072802.

 

  1. Karantzoulis E, Carniel A, Castronovo D, Di Mitri S, Diviacco B, Krecic S. Elettra and Elettra 2.0. Proceedings of the 12th International Particle Accelerator Conference. 2021;IPAC2021:3 pages, 0.511 MB.

 

  1. Karantzoulis E, Barletta W. Aspects of the Elettra 2.0 design. Nucl. Instrum. Methods Phys. Res. A, 2019 May;927:70–80.

 

  1. Karantzoulis E, Di Mitri S, Barbo F, Barletta W, Bassanese S, Bracco R, Design strategies and technology of Elettra 2.0 for a versatile offer to the user community. Nucl. Instrum. Methods Phys. Res. A, 2024 Mar;1060:169007.

 

  1. Baird S. Accelerators for pedestrians [Internet]. CERN. Geneva. AB Department; 2007 Feb p. 155. (Accelerators and Storage Rings). Report No.: AB-Note-2007-014; CERN-AB-Note-2007-014; PS-OP-Note-95-17-Rev-2; CERN-PS-OP-Note-95-17-Rev-2. Available from: https://cds.cern.ch/record/ 1017689/files/ab-note-2007-014.pdf.

 

  1. Chung Y, Decker G, Evans K. Closed orbit correction using singular value decomposition of the response matrix. In: Proceedings of International Conference on Particle Accelerators. 1993;3:2263–5.

 

  1. Borland M. ELEGANT: A flexible SDDS-compliant code for accelerator simulation [Internet]. 2000 Aug [cited 2024 Feb 3] LS-287, 761286. Report No.: LS-287, 761286. Available from: http://www.osti.gov/servlets/purl/761286/.

 

  1. PART THREE: Machine and Infrastructure [Internet]. Elettra-Sincrotrone Trieste; p. 469. Available from: https://www.elettra.eu/images/ Documents/ELETTRA%20Machine/Elettra2/TDR-Machine-Infrastructures-Final-compresso.pdf.

 

  1. Benjamin C, Golnaraghi F. Automatic control systems. John Wiley and Sons. 2010.

 

  1. Ogata K. Modern control engineering. Pearson, 5th edition. 2010.

 

  1. Limon D, Calliess J, Maciejowski J.M. Learning-based Nonlinear Model Predictive Control. IFAC-PapersOnLine. 2017;50(1):7769–76.

 

  1. Abiodun O.I, Jantan A, Omolara A.E, Dada K.V, Mohamed N.A, Arshad H. State-of-the-art in artificial neural network applications: A survey. Heliyon. 2018 Nov;4(11):e00938.

 

  1. Emami S.A, Castaldi P, Banazadeh A. Neural network-based flight control systems: Present and future. Annual Reviews in Control. 2022;53:97–137.

 

  1. Li Z, Yang W, Peng S, Liu F. A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects. CoRR [Internet]. 2020;abs/2004.02806. Available from: https://arxiv.org/abs/2004.02806.

 

  1. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015 May 28;521(7553):436–44.

 

  1. Kiranyaz S, Avci O, Abdeljaber O, Ince T, Gabbouj M, Inman D.J. 1D convolutional neural networks and applications: A survey. Mechanical Systems and Signal Processing. 2021 Apr;151:107398.

 

  1. Smola A.J, Schölkopf B. A tutorial on support vector regression. Statistics and computing. 2004;14:199–222.

 

  1. Borchani H, Varando G, Bielza C, Larranaga P. A survey on multi-output regression. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2015;5(5):216–33.

Home