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Title: Automated analysis for detecting beams in laser wakefield simulations

Automated analysis for detecting beams in laser wakefield simulations 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:
OSTI Identifier:939132
Report Number(s):LBNL-960E
TRN: US0806181
DOE Contract Number:DE-AC02-05CH11231
Resource Type:Conference
Data Type:
Resource Relation:Conference: The 2008 International Conference on Machine Learning and Applications ( ICMLA'08) , San Diego, California, USA, December 11-13, 2008
Research Org:Ernest Orlando Lawrence Berkeley National Laboratory, Berkeley, CA (US)
Sponsoring Org:Computational Research Division; National Energy Research Scientific Computing Division; Physics Division
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