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 n...

• Type: Conference
• Year: 2008
• Where: ICMLA
• Authors: Daniela Mayumi Ushizima, Oliver Rübel, Prabhat, Gunther H. Weber, E. Wes Bethel, Cecilia R. Aragon, Cameron G. R. Geddes, Estelle Cormier-Michel, Bernd Hamann, Peter Messmer, Hans Hagen
• Keywords: Fuzzy Clustering, ICMLA 2008, Machine Learning, Particle Acceleration Distance, Wakefield Particle Accelerators