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Author: X. Yang


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Reference
X. Yang, A. A. Derbenev, R. P. Fliller, T. V. Shaftan, and V. V. Smaluk, “Development of New Operational Mode for NSLS-II Injector: Low Energy 100MeV Linac-to-Booster Injection”, in Proc. 9th Int. Particle Accelerator Conf. (IPAC'18), Vancouver, Canada, Apr.-May 2018, pp. 1330-1333.
X. Yang et al., “Visualizing Lattice Dynamic Behavior by Acquiring a Single Time-Resolved MeV”, in Proc. 12th Int. Particle Accelerator Conf. (IPAC'21), Campinas, Brazil, May 2021, pp. 1311-1314.
X. Yang et al., “Tuning Quadrupoles for Brighter and Sharper Ultra-fast Electron Diffraction Imaging”, in Proc. 10th Int. Particle Accelerator Conf. (IPAC'19), Melbourne, Australia, May 2019, pp. 3571-3573.
X. Yang et al., “The Terahertz FEL Facility Project at CAEP”, in Proc. 34th Int. Free Electron Laser Conf. (FEL'12), Nara, Japan, Aug. 2012, paper WEPD49, pp. 484-486.
X. Yang et al., “The Status of the Ultra-High Vacuum System of HIRFL-CSR”, in Proc. 3rd Asian Particle Accelerator Conf. (APAC'04), Gyeongju, Korea, Mar. 2004, paper THP16002, pp. XX-XX.
X. Yang et al., “Online Optimization of NSLS-II Dynamic Aperture and Injection Transient”, in Proc. 13th Int. Particle Accelerator Conf. (IPAC'22), Bangkok, Thailand, Jun. 2022, pp. 1159-1162.
X. Yang et al., “Interferometric Measurement of Bunch Length of a 3Mev Picocoulomb Electron Beam”, in Proc. 10th Int. Particle Accelerator Conf. (IPAC'19), Melbourne, Australia, May 2019, pp. 2766-2769.
X. Yang et al., “Experimental Demonstration of Wideband Tunability of an Ultrafast Laser-Seeded Free-Electron Laser”, in Proc. 32nd Int. Free Electron Laser Conf. (FEL'10), Malmö, Sweden, Aug. 2010, paper TUPB23, pp. 302-305.
X. Yang et al., “Algebraic Reconstruction of Ultrafast Tomography Images at the Large Scale Data Facility”, in Proc. 14th Int. Conf. on Accelerator and Large Experimental Physics Control Systems (ICALEPCS'13), San Francisco, CA, USA, Oct. 2013, paper WECOBA01, pp. 996-999.
X. Yang et al., “Accurate prediction of mega-electron-volt electron beam properties from UED using machine learning”, in Proc. 14th Int. Particle Accelerator Conf. (IPAC'23), Venice, Italy, May 2023, paper THPL014, pp. 4452-4455.


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