Issue No. 02 - Feb. (2014 vol. 20)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2013.107
Oliver Rubel , Comput. Res. Div., Lawrence Berkeley Nat. Lab. (LBNL), Berkeley, CA, USA
Cameron G. R. Geddes , Accel. & Fusion Res. Div., Lawrence Berkeley Nat. Lab. (LBNL), Berkeley, CA, USA
Min Chen , Dept. of Phys. & Astron., Shanghai Jiao Tong Univ., Shanghai, China
Estelle Cormier-Michel , Tech-X Corp., Boulder, CO, USA
E. Wes Bethel , Comput. Res. Div., Lawrence Berkeley Nat. Lab. (LBNL), Berkeley, CA, USA
Plasma-based particle accelerators can produce and sustain thousands of times stronger acceleration fields than conventional particle accelerators, providing a potential solution to the problem of the growing size and cost of conventional particle accelerators. To facilitate scientific knowledge discovery from the ever growing collections of accelerator simulation data generated by accelerator physicists to investigate next-generation plasma-based particle accelerator designs, we describe a novel approach for automatic detection and classification of particle beams and beam substructures due to temporal differences in the acceleration process, here called acceleration features. The automatic feature detection in combination with a novel visualization tool for fast, intuitive, query-based exploration of acceleration features enables an effective top-down data exploration process, starting from a high-level, feature-based view down to the level of individual particles. We describe the application of our analysis in practice to analyze simulations of single pulse and dual and triple colliding pulse accelerator designs, and to study the formation and evolution of particle beams, to compare substructures of a beam, and to investigate transverse particle loss.
Feature extraction, Acceleration, Particle beams, Linear particle accelerator, Plasmas, Analytical models, Plasma waves
O. Rubel, C. G. Geddes, Min Chen, E. Cormier-Michel and E. W. Bethel, "Feature-Based Analysis of Plasma-Based Particle Acceleration Data," in IEEE Transactions on Visualization & Computer Graphics, vol. 20, no. 2, pp. 196-210, 2014.