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Author: R. Roussel


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Reference
R. Roussel et al., “Efficient tuning of particle accelerator emittance via Bayesian algorithm execution and virtual objectives”, presented at the 14th Int. Particle Accelerator Conf. (IPAC'23), Venice, Italy, May 2023, paper MOOD3, unpublished.
D. Storey et al., “Status and first results from FACET-II towards the demonstration of plasma wakefield acceleration, coherent radiation generation, and probing strong-field QED”, in Proc. 14th Int. Particle Accelerator Conf. (IPAC'23), Venice, Italy, May 2023, paper TUPA104, pp. 1549-1552.
J. P. Gonzalez-Aguilera, Y.-K. Kim, R. Roussel, A. Edelen, and C. Mayes, “Towards fully differentiable accelerator modeling”, in Proc. 14th Int. Particle Accelerator Conf. (IPAC'23), Venice, Italy, May 2023, paper WEPA065, pp. 2797-2800.
R. Robles, A. Edelen, W. Lou, C. Mayes, and R. Roussel, “Efficient computation of two-dimensional coherent synchrotron radiation with neural networks”, in Proc. 14th Int. Particle Accelerator Conf. (IPAC'23), Venice, Italy, May 2023, paper WEPA105, pp. 2892-2895.
A. Edelen et al., “Detailed Phase Space Reconstruction from Accelerator Beam Measurements Using Differentiable Simulations”, presented at the 14th Int. Particle Accelerator Conf. (IPAC'23), Venice, Italy, May 2023, paper THPL165, unpublished.
R. Roussel, C. Mayes, A. Edelen, and A. Bartnik, “Xopt: A simplified framework for optimization of accelerator problems using advanced algorithms”, in Proc. 14th Int. Particle Accelerator Conf. (IPAC'23), Venice, Italy, May 2023, paper THPL164, pp. 4847-4850.
S. Kim et al., “Demonstrations of the 4D phase space reconstruction of flat and magnetized beams using neural-networks and differentiable simulations”, presented at the 15th Int. Particle Accelerator Conf. (IPAC'24), Nashville, TN, USA, May 2024, paper MOPS77, unpublished.
A. Edelen et al., “Progress on combining digital twins and machine learning-based control for accelerators at SLAC”, in Proc. 15th Int. Particle Accelerator Conf. (IPAC'24), Nashville, TN, USA, May 2024, paper TUPS72, pp. 1846-1849.
R. Roussel et al., “Efficient 6-dimensional phase space reconstructions from experimental measurements using generative machine learning”, presented at the 15th Int. Particle Accelerator Conf. (IPAC'24), Nashville, TN, USA, May 2024, paper TUPS73, unpublished.
J. P. Gonzalez-Aguilera, R. Roussel, A. Edelen, and Y.-K. Kim, “Detailed characterization of coherent synchrotron radiation effects using generative phase space reconstruction”, in Proc. 15th Int. Particle Accelerator Conf. (IPAC'24), Nashville, TN, USA, May 2024, paper WEPG94, pp. 2442-2445.


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