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Author: K. Brown


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Reference
W. Lin, C. Kelly, G. Hoffstaetter, K. Brown, and N. Urban, “Machine learning assisted Bayesian calibration of accelerator digital twin from orbit response data”, in Proc. 6th North American Particle Accelerator Conference (NAPAC'25), Sacramento, California, USA, Aug. 2025, pp. 177-181.
E. Hamwi et al., “Minimizing dispersion through resonant extraction for BNL's NSRL”, in Proc. 6th North American Particle Accelerator Conference (NAPAC'25), Sacramento, California, USA, Aug. 2025, pp. 515-518.
L. Hajdu et al., “Image processing with ML for automated tuning of the NASA Space Radiation Laboratory beam line”, in Proc. 20th International Conference on Accelerator and Large Experimental Control Systems (ICALEPCS'25), Chicago, IL, USA, Sep. 2025, pp. 1008-1012.
L. Hajdu et al., “Image processing with ML for automated tuning of the NASA Space Radiation Laboratory beam line”, presented at the 20th International Conference on Accelerator and Large Experimental Control Systems (ICALEPCS'25), Chicago, IL, USA, Sep. 2025, paper WEPD070, unpublished.
W. Lin et al., “Digital twin development for the NASA Space Radiation Laboratory”, presented at the 17th Int. Particle Accelerator Conf. (IPAC'26), Deauville, France, May 2026, paper MOP6352, this conference.
A. Brynes et al., “Progress in the development of the community Particle Accelerator Language Standard (PALS)”, presented at the 17th Int. Particle Accelerator Conf. (IPAC'26), Deauville, France, May 2026, paper MOP6394, this conference.
Y. Gao et al., “Longitudinal phase space diagnostics for the AGS”, presented at the 17th Int. Particle Accelerator Conf. (IPAC'26), Deauville, France, May 2026, paper MOP6393, this conference.
T. Hellert et al., “Agentic AI as a Middle Layer for Accelerator Control: Multi-Facility Deployment and Early Results”, presented at the 17th Int. Particle Accelerator Conf. (IPAC'26), Deauville, France, May 2026, paper MOP6324, this conference.
J.-L. Vay et al., “Overview of the US DOE Multi-Office particle Accelerator Team (MOAT) project”, presented at the 17th Int. Particle Accelerator Conf. (IPAC'26), Deauville, France, May 2026, paper MOV6302, this conference.
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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