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Statistical lattice design and topological selection of a 300–600 MeV scaling fixed-field alternating gradient proton driver at CSNS Phase-II upgrade

Bin Wu1,2,*, WenJie Han1,2, Yan Cui1,2,3, MingYang Huang1,2, Kai Zhou1,2, HanYang Liu1,2, ShouYan Xu1,2, JiaJie Tan1,2, YuWen An1,2 et al.

YanLiang Han1,2, Yong Li1,2, LiangSheng Huang1,2, Jian Liang Chen1,2, Sheng Wang1,2, and Xiao Li1,2,†

  • *Contact author: wubin@ihep.ac.cn
  • †Contact author: lixiao@ihep.ac.cn

Phys. Rev. Accel. Beams 29, 093701 – Published 15 September, 2026

DOI: https://doi.org/10.1103/v699-f652

Abstract

A 300–600 MeV high-intensity scaling fixed-field alternating gradient (FFA) facility is proposed as a multipurpose proton driver for the China Spallation Neutron Source (CSNS) Phase-II upgrade. To identify a viable lattice configuration under strict engineering constraints, a statistical analysis methodology based on large-sample ray-tracing simulations comprising over 360,000 parameter configurations is applied. A two-stage scanning pipeline evaluates the topological limits of both FDF triplet and FD doublet structures under realistic multisource error budgets. Factoring in phase-space geometry, error tolerance, and facility integration, the findings indicate that a spiral FD doublet lattice with a super-periodicity of N=16 is preferred as the structural baseline; it avoids parameter bottlenecks and yields a large, resonance-sparse stable region. To characterize the coupled high-dimensional dynamics quantitatively, a machine learning feature attribution framework is applied. The framework identifies the spiral edge angle and the full-ring vertical tune as the primary drivers of beam survivability, and maps the underlying resonance stopbands from tracking data alone. Based on these results, a baseline configuration is established with a wide dynamic tune allowance that maintains long-term beam survivability within the physical and engineering boundaries of the facility.

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References (27)

  1. J. Wei, H. S. Chen, Y. W. Chen et al., China spallation neutron source: Design, R&D, and outlook, Nucl. Instrum. Methods Phys. Res., Sect. A 600, 10 (2009).
  2. S. Wang, Y. W. An, S. X. Fang et al., An overview of design for CSNS/RCS and beam transport, Sci. China Phys. Mech. Astron. 54, 239 (2011).
  3. B. Qin and Y. Mori, Compact superferric FFAG accelerators for medium energy hadron applications, Nucl. Instrum. Methods Phys. Res., Sect. A 648, 28 (2011).
  4. J. Y. Tang, Q. An, J. B. Bai et al., Back-n white neutron source at CSNS and its applications, Nucl. Sci. Tech. 32, 11 (2021).
  5. S. Machida, R. Barlow, J. S. Berg et al., Acceleration in the linear non-scaling fixed-field alternating-gradient accelerator EMMA, Nat. Phys. 8, 243 (2012).
  6. K. R. Symon, D. W. Kerst, L. W. Jones, L. J. Laslett, and K. M. Terwilliger, Fixed-field alternating-gradient particle accelerators, Phys. Rev. 103, 1837 (1956).
  7. Y. Mori, Y. Ishi, Y. Kuriyama, B. Qin, T. Uesugi et al., Present status of FFAG proton accelerators at KURRI, in Proc. IPAC’11 (JACoW Publishing, Geneva, Switzerland, 2011), pp. 2685–2687.
  8. J.-B. Lagrange et al., Progress on design studies for the ISIS II upgrade, in Proc. IPAC’19 (JACoW Publishing, Geneva, Switzerland, 2019), pp. 2075–2078.
  9. S. Antoine, B. Autin, W. Beeckman, J. Collot, M. Conjat, F. Forest, J. Fourrier, E. Froidefond, J. L. Lancelot, J. Mandrillon, P. Mandrillon, F. Méot, Y. Mori, D. Neuvéglise, C. Ohmori, J. Pasternak, and T. Planche, Principle design of a protontherapy, rapid-cycling, variable energy spiral FFAG, Nucl. Instrum. Methods Phys. Res., Sect. A 602, 293 (2009).
  10. K. J. Peach et al., Conceptual design of a nonscaling fixed field alternating gradient accelerator for protons and carbon ions for charged particle therapy, Phys. Rev. ST Accel. Beams 16, 030101 (2013).
  11. S. Machida, Scaling fixed-field alternating-gradient accelerators with reverse bend and spiral edge angle, Phys. Rev. Lett. 119, 064802 (2017).
  12. Y. Li, S. Y. Xu, Y. S. Yuan, L. S. Huang, and S. Wang, Study for space charge effect in tune space at CSNS-II/RCS, J. Phys. Conf. Ser. 2687, 052018 (2024).
  13. H. A. Enge, Effect of extended fringing fields on ion-focusing properties of deflecting magnets, Rev. Sci. Instrum. 35, 278 (1964).
  14. J.-B. Lagrange, The Particle Tracking Code Fixfield, in Proc. IPAC’21, International Particle Accelerator Conference No. 12 (JACoW Publishing, Geneva, Switzerland, 2021), pp. 1905–1906.
  15. F. Méot, The ray-tracing code Zgoubi–status, Nucl. Instrum. Methods Phys. Res., Sect. A 767, 112 (2014).
  16. A. Adelmann et al., OPAL a versatile tool for charged particle accelerator simulations, arXiv:1905.06654.
  17. I. Rodriguez et al., FFA magnet prototype for high intensity pulsed proton driver, in Proc. IPAC’23, IPAC’23—14th International Particle Accelerator Conference No. 14 (JACoW Publishing, Geneva, Switzerland, 2023), pp. 2261–2264.
  18. M. D. McKay, R. J. Beckman, and W. J. Conover, A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics 21, 239 (1979).
  19. S. Tygier, R. B. Appleby, J. M. Garland, K. Hock, H. Owen, D. J. Kelliher, and S. L. Sheehy, The PyZgoubi framework and the simulation of dynamic aperture in fixed-field alternating-gradient accelerators, Nucl. Instrum. Methods Phys. Res., Sect. A 775, 15 (2015).
  20. S.-Y. Lee, Accelerator Physics, 4th ed. (World Scientific Publishing Company, Singapore, 2018).
  21. J. Fourrier, F. Martinache, F. Méot, and J. Pasternak, Spiral FFAG lattice design tools. Application to 6-D tracking in a proton-therapy class lattice, Nucl. Instrum. Methods Phys. Res., Sect. A 589, 133 (2008).
  22. M. Topp-Mugglestone et al., Nonlinear dynamics of scaling FFAs, in Proc. IPAC’23 (JACoW Publishing, Geneva, Switzerland, 2023), pp. 3367–3370.
  23. A. Edelen, C. Mayes, D. Bowring, D. Ratner, A. Adelmann, R. Ischebeck, J. Snuverink, I. Agapov, R. Kammering, J. Edelen, I. Bazarov, G. Valentino, and J. Wenninger, Opportunities in machine learning for particle accelerators, arXiv:1811.03172.
  24. J. Laskar, Frequency analysis of a dynamical system, Celestial Mech. Dyn. Astron. 56, 191 (1993).
  25. Y. Papaphilippou, Detecting chaos in particle accelerators through the frequency map analysis method, Chaos 24, 024412 (2014).
  26. T. Chen and C. Guestrin, Xgboost: A scalable tree boosting system, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16 (Association for Computing Machinery, New York, NY, USA, 2016), pp. 785–794.
  27. S. M. Lundberg and S.-I. Lee, A unified approach to interpreting model predictions, in Advances in Neural Information Processing Systems 30 (NeurIPS 2017) (2017), pp. 4768–4777.

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