A Differentiable Surrogate for Loss-Dominated Ion Beam Transport

Abstract

Radioactive nuclei with extreme proton-to-neutron ratios provide unique probes of nuclear structure, fundamental symmetries, astrophysical processes, and applications such as medicine. However, their short half-lives and low production rates mean that experiments must work with few desired ions, often amid contaminant backgrounds exceeding the signal by six orders of magnitude. Efficiently isolating, transporting, and delivering these ions through multi-stage apparatus is therefore a central challenge. We introduce a differentiable probabilistic surrogate for ion-beam transport in regimes dominated by particle loss. The model jointly captures the survival probability of ions along the beamline and the evolving phase-space distribution of the surviving particles by combining discrete-time survival analysis with conditional normalizing flows. Applied to a high-dimensional electrostatic beamline, the resulting likelihood enables gradient-based optimization of operating voltages and inference of beam properties from sparse detector observations. Once trained, the surrogate provides millisecond-scale differentiable predictions, transforming particle-tracking simulations with hard losses into an inference-ready model for experimental design, beamline optimization, and future digital twins of experiments with rare nuclei.

A Differentiable Neural Surrogate for Photon Propagation in Neutrino Telescopes
Felix J. Yu, Berthy T. Feng, Nicholas Kamp +1
arXiv4 Sep 2026PreprintCC BY 4.0Full textHigh-energy astrophysics High-energy & particle physicsarXiv
Fast and Differentiable MHD Surrogate for Solar Wind using Neural Operator
Prateek Mayank, Enrico Camporeale, Thomas E. Berger +2
arXiv5 Oct 2026PreprintCC BY 4.0Full textSolar & stellar physics Fluid dynamicsarXiv
A differentiable PCA-GPR surrogate framework for full-transient Bayesian inverse uncertainty quantification of black-box nuclear thermal-hydraulic systems
Zikang Yaoa, Eugene Yeea, Fue-Sang Lien
arXiv14 Oct 2026PreprintarXiv licenceFull textComputational physics Fluid dynamicsarXiv
Physics-constrained graph closures and working-point conditioning for a fast surrogate of linac beam dynamics
Bowen Zhang, Dazhang Huang, Nengyuan Zhang +3
arXiv25 Oct 2026PreprintCC BY 4.0Full textApplied physics & instrumentationarXiv
A neural characteristic mapping method: Lagrangian PINNs based on flow maps for transport-dominated problems
Martin Campos Pinto, Victor Fournet, Emmanuel Franck +2
arXiv8 Oct 2026PreprintCC BY 4.0Full textNumerical analysis & scientific computing Computational physicsarXiv

Cite

BibTeX RIS

BibTeX

@misc{munozarias2026differentiable,
  title = {{A Differentiable Surrogate for Loss-Dominated Ion Beam Transport}},
  author = {Jose M. Munoz-Arias and Laura Ruiz-Arango and Derick Gonzalez-Acevedo and Fabian C. Pastrana-Cruz and Jackson Hacias and Ronald F. Garcia Ruiz},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.08877},
  eprint = {2610.08877},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.08877},
}

RIS

TY  - UNPB
TI  - A Differentiable Surrogate for Loss-Dominated Ion Beam Transport
AU  - Jose M. Munoz-Arias
AU  - Laura Ruiz-Arango
AU  - Derick Gonzalez-Acevedo
AU  - Fabian C. Pastrana-Cruz
AU  - Jackson Hacias
AU  - Ronald F. Garcia Ruiz
PY  - 2026
DA  - 2026/10/06
PB  - arXiv
DO  - 10.48550/arxiv.2610.08877
UR  - https://arxiv.org/abs/2610.08877
AB  - Radioactive nuclei with extreme proton-to-neutron ratios provide unique probes of nuclear structure, fundamental symmetries, astrophysical processes, and applications such as medicine. However, their short half-lives and low production rates mean that experiments must work with few desired ions, often amid contaminant backgrounds exceeding the signal by six orders of magnitude. Efficiently isolating, transporting, and delivering these ions through multi-stage apparatus is therefore a central challenge. We introduce a differentiable probabilistic surrogate for ion-beam transport in regimes dominated by particle loss. The model jointly captures the survival probability of ions along the beamline and the evolving phase-space distribution of the surviving particles by combining discrete-time survival analysis with conditional normalizing flows. Applied to a high-dimensional electrostatic beamline, the resulting likelihood enables gradient-based optimization of operating voltages and inference of beam properties from sparse detector observations. Once trained, the surrogate provides millisecond-scale differentiable predictions, transforming particle-tracking simulations with hard losses into an inference-ready model for experimental design, beamline optimization, and future digital twins of experiments with rare nuclei.
ER  -

Abstract

Related

Cite