Automated analysis for detecting beams in laser wakefield simulations

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Laser wakefield particle accelerators have shown the potential to generate electric fields thousands of times higher than those of conventional accelerators. The resulting extremely short particle acceleration distance could yield a potential new compact source of energetic electrons and radiation, with wide applications from medicine to physics. Physicists investigate laser-plasma internal dynamics by running particle-in-cell simulations; however, this generates a large dataset that requires time-consuming, manual inspection by experts in order to detect key features such as beam formation. This paper describes a framework to automate the data analysis and classification of simulation data. First, we propose a new method ... continued below

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Ushizima, Daniela M.; Rubel, Oliver; Prabhat, Mr.; Weber, Gunther H.; Bethel, E. Wes; Aragon, Cecilia R. et al. July 3, 2008.

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Laser wakefield particle accelerators have shown the potential to generate electric fields thousands of times higher than those of conventional accelerators. The resulting extremely short particle acceleration distance could yield a potential new compact source of energetic electrons and radiation, with wide applications from medicine to physics. Physicists investigate laser-plasma internal dynamics by running particle-in-cell simulations; however, this generates a large dataset that requires time-consuming, manual inspection by experts in order to detect key features such as beam formation. This paper describes a framework to automate the data analysis and classification of simulation data. First, we propose a new method to identify locations with high density of particles in the space-time domain, based on maximum extremum point detection on the particle distribution. We analyze high density electron regions using a lifetime diagram by organizing and pruning the maximum extrema as nodes in a minimum spanning tree. Second, we partition the multivariate data using fuzzy clustering to detect time steps in a experiment that may contain a high quality electron beam. Finally, we combine results from fuzzy clustering and bunch lifetime analysis to estimate spatially confined beams. We demonstrate our algorithms successfully on four different simulation datasets.

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  • The 2008 International Conference on Machine Learning and Applications ( ICMLA'08) , San Diego, California, USA, December 11-13, 2008

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  • Report No.: LBNL-960E
  • Grant Number: DE-AC02-05CH11231
  • Office of Scientific & Technical Information Report Number: 939132
  • Archival Resource Key: ark:/67531/metadc896462

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  • July 3, 2008

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  • Sept. 27, 2016, 1:39 a.m.

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  • Nov. 8, 2016, 1:13 p.m.

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Ushizima, Daniela M.; Rubel, Oliver; Prabhat, Mr.; Weber, Gunther H.; Bethel, E. Wes; Aragon, Cecilia R. et al. Automated analysis for detecting beams in laser wakefield simulations, article, July 3, 2008; Berkeley, California. (digital.library.unt.edu/ark:/67531/metadc896462/: accessed December 13, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.