Automated detection and analysis of particle beams in laser-plasma accelerator simulations
Numerical simulations of laser-plasma wakefield (particle) accelerators model the acceleration of electrons trapped in plasma oscillations (wakes) left behind when an intense laser pulse propagates through the plasma. The goal of these simulations is to better understand the process involved in plasma wake generation and how electrons are trapped and accelerated by the wake. Understanding of such accelerators, and their development, offer high accelerating gradients, potentially reducing size and cost of new accelerators. One operating regime of interest is where a trapped subset of electrons loads the wake and forms an isolated group of accelerated particles with low spread in momentum and position, desirable characteristics for many applications. The electrons trapped in the wake may be accelerated to high energies, the plasma gradient in the wake reaching up to a gigaelectronvolt per centimeter. High-energy electron accelerators power intense X-ray radiation to terahertz sources, and are used in many applications including medical radiotherapy and imaging. To extract information from the simulation about the quality of the beam, a typical approach is to examine plots of the entire dataset, visually determining the adequate parameters necessary to select a subset of particles, which is then further analyzed. This procedure requires laborious examination of massive data sets over many time steps using several plots, a routine that is unfeasible for large data collections. Demand for automated analysis is growing along with the volume and size of simulations. Current 2D LWFA simulation datasets are typically between 1GB and 100GB in size, but simulations in 3D are of the order of TBs. The increase in the number of datasets and dataset sizes leads to a need for automatic routines to recognize particle patterns as particle bunches (beam of electrons) for subsequent analysis. Because of the growth in dataset size, the application of machine learning techniques for scientific data mining is increasingly considered. In plasma simulations, Bagherjeiran et al. presented a comprehensive report on applying graph-based techniques for orbit classification. They used the KAM classifier to label points and components in single and multiple orbits. Love et al. conducted an image space analysis of coherent structures in plasma simulations. They used a number of segmentation and region-growing techniques to isolate regions of interest in orbit plots. Both approaches analyzed particle accelerator data, targeting the system dynamics in terms of particle orbits. However, they did not address particle dynamics as a function of time or inspected the behavior of bunches of particles. Ruebel et al. addressed the visual analysis of massive laser wakefield acceleration (LWFA) simulation data using interactive procedures to query the data. Sophisticated visualization tools were provided to inspect the data manually. Ruebel et al. have integrated these tools to the visualization and analysis system VisIt, in addition to utilizing efficient data management based on HDF5, H5Part, and the index/query tool FastBit. In Ruebel et al. proposed automatic beam path analysis using a suite of methods to classify particles in simulation data and to analyze their temporal evolution. To enable researchers to accurately define particle beams, the method computes a set of measures based on the path of particles relative to the distance of the particles to a beam. To achieve good performance, this framework uses an analysis pipeline designed to quickly reduce the amount of data that needs to be considered in the actual path distance computation. As part of this process, region-growing methods are utilized to detect particle bunches at single time steps. Efficient data reduction is essential to enable automated analysis of large data sets as described in the next section, where data reduction methods are steered to the particular requirements of our clustering analysis. Previously, we have described the application of a set of algorithms to automate the data analysis and classification of particle beams in the LWFA simulation data, identifying locations with high density of high energy particles. These algorithms detected high density locations (nodes) in each time step, i.e. maximum points on the particle distribution for only one spatial variable. Each node was correlated to a node in previous or later time steps by linking these nodes according to a pruned minimum spanning tree (PMST). We call the PMST representation 'a lifetime diagram', which is a graphical tool to show temporal information of high dense groups of particles in the longitudinal direction for the time series. Electron bunch compactness was described by another step of the processing, designed to partition each time step, using fuzzy clustering, into a fixed number of clusters.
- Research Organization:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- Accelerator& Fusion Research Division; Computational Research Division
- DOE Contract Number:
- DE-AC02-05CH11231
- OSTI ID:
- 986491
- Report Number(s):
- LBNL-3845E; TRN: US1006367
- Country of Publication:
- United States
- Language:
- English
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