Speaker
Description
As next-generation particle accelerators aim for unprecedented beam intensities, modeling collective effects—particularly space charge—has become a dominant challenge. In low-energy, high-intensity hadron colliders, space charge has been demonstrated to cause tune shifts, emittance growth, halo formation, and particle losses. Consequently, accurate modeling is essential for machine design and operation [1-6].
To address this, the proposed work develops an efficient framework for incorporating space-charge effects throughout the entire accelerator lattice. The tool utilizes a 2.5D symplectic space-charge map, previously demonstrated to accurately reproduce the six-dimensional phase space evolution while preserving symplecticity in Electron-Ion Collider (EIC) linear one-turn map simulations [7-8].
This model has then been extended to the full realistic ring-lattice by applying symplectic space-charge kicks at multiple integration points. Powered by a GPU-based implementation for large-scale tracking, this allows us to simulate multiple turns and investigate the interplay among space-charge forces, beam-beam interactions, and high-order multipole resonances.
Furthermore, recent studies demonstrate that neural networks can serve as surrogate models to accelerate computationally heavy workflows [9-11]. Based on this strategy, we present a preliminary study using a customized feed-forward neural network as a fast-tracking surrogate model. Because this network is symplectic by construction, it strictly guarantees the preservation of phase-space volume and long-term stability at every beam turn in hadron ring lattices.
Combining this physics-informed AI with high-speed tracking enables us to study the long-term interplay between space charge and beam-beam interactions in the presence of high-order nonlinear magnetic forces.
Ultimately, by efficiently identifying nonlinear resonances and predicting their impact on beam stability, this framework would represent a critical step forward in developing optimization tools for complex accelerator environments.
[1]Benedikt, M., Mertens, V., Cerutti, F., Riegler, W., Otto, T., Tommasini, D., Tavian, L.J., Gutleber, J., Zimmermann, F., Mangano, M. and Goddard, B., 2018. FCC-hh: The Hadron Collider: future circular collider conceptual design report volume 3. Eur. Phys. J. Spec. Top., 228(CERN-ACC-2018-0058), pp.755-1107.
[2] Kenneth R. Long, Donatella Lucchesi, Mark A. Palmer, Nadia Pastrone, Daniel Schulte, and V. Shiltsev, Muon colliders to expand frontiers of particle physics, Nat. Phys. 17, 289 (2021).
[3]Vladimir Shiltsev and Frank Zimmermann, Modern and future colliders, Rev. Mod. Phys. 93, 015006 (2021).
[4]Hofmann, I. and Boine-Frankenheim, O., 2015. Space-charge structural instabilities and resonances in high-intensity beams. Physical Review Letters, 115(20), p.204802.
[5]Laslett, L.J., 1963. On intensity limitations imposed by transverse space-charge effects in circular particle accelerators. Summer Study on Storage Rings, BNL Report, 7534, pp.325-367.
[6]Li, S., Luo, Q., Liu, T., Zhang, L., Zou, Y. and Ohmi, K., Preliminary study on Beam-Beam interaction with Multi-Physics Effects in the Super Tau-Charm Facility.
[7]Qiang, J., 2025. Two-and-a-half dimensional symplectic space-charge solver. Physical Review Accelerators and Beams, 28(11), p.114602.
[8]Alamprese, H., Hao, Y., Qiang, J., Preliminary study of Space Charge and Beam-Beam interplay in a collider ring, North America Particle Accelerator Conference, August 2025.
[9]Edelen, A., Neveu, N., Frey, M., Huber, Y., Mayes, C. and Adelmann, A., 2020. Machine learning for orders of magnitude speedup in multiobjective optimization of particle accelerator systems. Physical Review Accelerators and Beams, 23(4), p.044601.
[10]Huang, C.K., Tang, Q., Batygin, Y.K., Beznosov, O., Burby, J., Kim, A., Kurennoy, S., Kwan, T. and Rakotoarivelo, H.N., 2024, January. Symplectic neural surrogate models for beam dynamics. In Journal of Physics: Conference Series (Vol. 2687, No. 6, p. 062026). IOP Publishing.
[11]Wan, J., Qiang, J. and Hao, Y., 2025. Symplectic machine learning model for fast simulation of space-charge effects. Physical Review Accelerators and Beams, 28(7), p.074602.