Demonstration of machine learning-enhanced multi-objective optimization of ultrahigh-brightness lattices for 4th-generation synchrotron light sources

Lu, Y.; Leemann, S. C.; Sun, C.; Ehrlichman, M. P.; Nishimura, H.; Venturini, M.; Hellert, T.

doi:10.1016/j.nima.2023.168192

Demonstration of machine learning-enhanced multi-objective optimization of ultrahigh-brightness lattices for 4th-generation synchrotron light sources

Journal Article · Mon May 01 00:00:00 EDT 2023 · Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment

DOI:https://doi.org/10.1016/j.nima.2023.168192· OSTI ID:1962272

; ; ; Ehrlichman, M. P.; Nishimura, H.; Venturini, M.; Hellert, T.

Not Available

View Journal Article

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1962272

Alternate ID(s):: OSTI ID: 1963675

Journal Information:: Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment, Journal Name: Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment Journal Issue: C Vol. 1050; ISSN 0168-9002

Publisher:: ElsevierCopyright Statement

Country of Publication:: Netherlands

Language:: English

References (16)

A tutorial on multiobjective optimization: fundamentals and evolutionary methods Emmerich, Michael T. M.; Deutz, André H. Natural Computing, Vol. 17, Issue 3 https://doi.org/10.1007/s11047-018-9685-y	journal	May 2018
Global optimization of an accelerator lattice using multiobjective genetic algorithms Yang, Lingyun; Robin, David; Sannibale, Fernando Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 609, Issue 1 https://doi.org/10.1016/j.nima.2009.08.027	journal	October 2009
The anti-bend cell for ultralow emittance storage ring lattices Streun, A. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 737 https://doi.org/10.1016/j.nima.2013.11.064	journal	February 2014
Storage ring nonlinear dynamics optimization with multi-objective multi-generation Gaussian process optimizer Song, Minghao; Huang, Xiaobiao; Spentzouris, Linda Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 976 https://doi.org/10.1016/j.nima.2020.164273	journal	October 2020
Deep learning LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey Nature, Vol. 521, Issue 7553 https://doi.org/10.1038/nature14539	journal	May 2015
Genetic algorithm enhanced by machine learning in dynamic aperture optimization Li, Yongjun; Cheng, Weixing; Yu, Li Hua Physical Review Accelerators and Beams, Vol. 21, Issue 5 https://doi.org/10.1103/PhysRevAccelBeams.21.054601	journal	May 2018
Machine learning for orders of magnitude speedup in multiobjective optimization of particle accelerator systems Edelen, Auralee; Neveu, Nicole; Frey, Matthias Physical Review Accelerators and Beams, Vol. 23, Issue 4 https://doi.org/10.1103/PhysRevAccelBeams.23.044601	journal	April 2020
Neural network-based multiobjective optimization algorithm for nonlinear beam dynamics Wan, Jinyu; Chu, Paul; Jiao, Yi Physical Review Accelerators and Beams, Vol. 23, Issue 8 https://doi.org/10.1103/PhysRevAccelBeams.23.081601	journal	August 2020
Multiobjective optimization of the dynamic aperture using surrogate models based on artificial neural networks Kranjčević, M.; Riemann, B.; Adelmann, A. Physical Review Accelerators and Beams, Vol. 24, Issue 1 https://doi.org/10.1103/PhysRevAccelBeams.24.014601	journal	January 2021
Lifetime and Beam Size in a Storage Ring Bernardini, C.; Corazza, G. F.; Di Giugno, G. Physical Review Letters, Vol. 10, Issue 9 https://doi.org/10.1103/PhysRevLett.10.407	journal	May 1963
Beam dynamics and expected performance of Sweden’s new storage-ring light source: MAX IV Leemann, S. C.; Andersson, Å.; Eriksson, M. Physical Review Special Topics - Accelerators and Beams, Vol. 12, Issue 12 https://doi.org/10.1103/PhysRevSTAB.12.120701	journal	December 2009
Simultaneous optimization of beam emittance and dynamic aperture for electron storage ring using genetic algorithm Gao, Weiwei; Wang, Lin; Li, Weimin Physical Review Special Topics - Accelerators and Beams, Vol. 14, Issue 9 https://doi.org/10.1103/PhysRevSTAB.14.094001	journal	September 2011
Small-emittance and low-beta lattice designs and optimizations Sun, C.; Robin, D. S.; Nishimura, H. Physical Review Special Topics - Accelerators and Beams, Vol. 15, Issue 5 https://doi.org/10.1103/PhysRevSTAB.15.054001	journal	May 2012
Multivariate optimization of a high brightness dc gun photoinjector Bazarov, Ivan V.; Sinclair, Charles K. Physical Review Special Topics - Accelerators and Beams, Vol. 8, Issue 3 https://doi.org/10.1103/PhysRevSTAB.8.034202	journal	March 2005
A fast and elitist multiobjective genetic algorithm: NSGA-II Deb, K.; Pratap, A.; Agarwal, S. IEEE Transactions on Evolutionary Computation, Vol. 6, Issue 2 https://doi.org/10.1109/4235.996017	journal	April 2002
Touschek lifetime calculations and simulations for NSLS-II Montag, C.; Bengtsson, J.; Nash, B. 2007 IEEE Particle Accelerator Conference (PAC) https://doi.org/10.1109/PAC.2007.4440106	conference	January 2007

Similar Records

Demonstration of Machine Learning-Based Model-Independent Stabilization of Source Properties in Synchrotron Light Sources

Journal Article · Tue Nov 05 23:00:00 EST 2019 · Physical Review Letters · OSTI ID:1573250

Demonstration of machine learning-enhanced multi-objective optimization of ultrahigh-brightness lattices for 4th-generation synchrotron light sources

Citation Formats

References (16)

Similar Records

Related Subjects