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[n] A. A. Pryanichnikov et al., “The PIPLAN Proton-Carbon Ion Radiation Therapy Planning System”, in Proc. RuPAC2021, Alushta, Crimea, Sep.-Oct. 2021, pp. 179-181. doi:10.18429/JACoW-RuPAC2021-MOPSA40
[n] M. Noll et al., “Characterizing optical synchrotron radiation in the geometric optical phase space and optimizing the energy transport to a photo detector”, in Proc. IPAC'24, Nashville, TN, USA, May 2024, pp. 2358-2360. doi:10.18429/JACoW-IPAC2024-WEPG58
[n] S. Döbert, “Gradient Limitations for High-Frequency Accelerators”, in Proc. LINAC'04, Lübeck, Germany, Aug. 2004, paper WE101, pp. 513-517.
[n] W. Lin et al., “Machine-Learning–Assisted Bayesian Uncertainty Quantification for Accelerator Digital Twin Modeling and Control”, presented at the IPAC'26, Deauville, France, May 2026, paper MOP6310, unpublished.
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References
- A. A. Pryanichnikov et al., “The PIPLAN Proton-Carbon Ion Radiation Therapy Planning System”, in Proc. 27th Russian Particle Accelerator Conference (RuPAC2021), Alushta, Crimea, Sep.-Oct. 2021, pp. 179-181.
- M. Noll et al., “Characterizing optical synchrotron radiation in the geometric optical phase space and optimizing the energy transport to a photo detector”, in Proc. 15th Int. Particle Accelerator Conf. (IPAC'24), Nashville, TN, USA, May 2024, paper WEPG58, pp. 2358-2360.
- S. Döbert, “Gradient Limitations for High-Frequency Accelerators”, in Proc. 22nd Linear Accelerator Conf. (LINAC'04), Lübeck, Germany, Aug. 2004, paper WE101, pp. 513-517.
- W. Lin et al., “Machine-Learning–Assisted Bayesian Uncertainty Quantification for Accelerator Digital Twin Modeling and Control”, presented at the 17th Int. Particle Accelerator Conf. (IPAC'26), Deauville, France, May 2026, paper MOP6310, this conference.
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