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Title: Automated analysis for detecting beams in laser wakefield simulations

Abstract

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.

Authors:
; ; ; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
Computational Research Division; National Energy Research Scientific Computing Division; Physics Division
OSTI Identifier:
939132
Report Number(s):
LBNL-960E
TRN: US0806181
DOE Contract Number:  
DE-AC02-05CH11231
Resource Type:
Conference
Resource Relation:
Conference: The 2008 International Conference on Machine Learning and Applications ( ICMLA'08) , San Diego, California, USA, December 11-13, 2008
Country of Publication:
United States
Language:
English
Subject:
43; 70; 97; ACCELERATION; ACCELERATORS; ALGORITHMS; CLASSIFICATION; DATA ANALYSIS; DETECTION; ELECTRIC FIELDS; ELECTRON BEAMS; ELECTRONS; LASERS; LEARNING; LIFETIME; ORGANIZING; PHYSICS; SPACE-TIME; TAIL ELECTRONS

Citation Formats

Ushizima, Daniela M., Rubel, Oliver, Prabhat, Mr., Weber, Gunther H., Bethel, E. Wes, Aragon, Cecilia R., Geddes, Cameron G.R., Cormier-Michel, Estelle, Hamann, Bernd, Messmer, Peter, and Hagen, Hans. Automated analysis for detecting beams in laser wakefield simulations. United States: N. p., 2008. Web.
Ushizima, Daniela M., Rubel, Oliver, Prabhat, Mr., Weber, Gunther H., Bethel, E. Wes, Aragon, Cecilia R., Geddes, Cameron G.R., Cormier-Michel, Estelle, Hamann, Bernd, Messmer, Peter, & Hagen, Hans. Automated analysis for detecting beams in laser wakefield simulations. United States.
Ushizima, Daniela M., Rubel, Oliver, Prabhat, Mr., Weber, Gunther H., Bethel, E. Wes, Aragon, Cecilia R., Geddes, Cameron G.R., Cormier-Michel, Estelle, Hamann, Bernd, Messmer, Peter, and Hagen, Hans. Thu . "Automated analysis for detecting beams in laser wakefield simulations". United States. https://www.osti.gov/servlets/purl/939132.
@article{osti_939132,
title = {Automated analysis for detecting beams in laser wakefield simulations},
author = {Ushizima, Daniela M. and Rubel, Oliver and Prabhat, Mr. and Weber, Gunther H. and Bethel, E. Wes and Aragon, Cecilia R. and Geddes, Cameron G.R. and Cormier-Michel, Estelle and Hamann, Bernd and Messmer, Peter and Hagen, Hans},
abstractNote = {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.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2008},
month = {7}
}

Conference:
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