Automatic beam path analysis of laser wakefield particle acceleration data

Abstract

Abstract.Numerical simulations of laser wakefield particle accelerators play a key role in the understanding of the complex acceleration process and in the design of expensive experimental facilities. As the size and complexity of simulation output grows, an increasingly acute challenge is the practical need for computational techniques that aid in scientific knowledge discovery. To that end, we present a set of data-understanding algorithms that work in concert in a pipeline fashion to automatically locate and analyze high energy particle bunches undergoing acceleration in very large simulation datasets. These techniques work cooperatively by first identifying features of interest in individual timesteps, then integrating features across timesteps, and based on the information derived perform analysis of temporally dynamic features. This combination of techniques supports accurate detection of particle beams enabling a deeper level of scientific understanding of physical phenomena than has been possible before. By combining efficient data analysis algorithms and state-of-the-art data management we enable high-performance analysis of extremely large particle datasets in 3D. We demonstrate the usefulness of our methods for a variety of 2D and 3D datasets and discuss the performance of our analysis pipeline.

Parallel I/O, analysis, and visualization of a trillion particle simulation

“…We have chosen to use a set of efficient bitmap indexing techniques in FastBit [47], [48] because they have been demonstrated to work well on scientific data [38], [33]. Rübel et al demonstrated the use of FastBit to accelerate query-driven visualization of laser plasma accelerator simulations containing on the order hundreds of millions of particles per timestep [33], [32]. Evaluation of queries for single files were performed in serial in these efforts.…”

Introduction

“…Bin queries are evaluated using: (1) per-bin bitvectors returned by FastBit, (2) FastBit queries, and (3) a baseline sequential scan method. Image source: Rübel et al, 2010 [27]. tion methods-require the ability to evaluate bin-queries efficiently [27].…”

Coupling visualization and data analysis for knowledge discovery from multi-dimensional scientific data

“…Automating the detection of particle beams supports a more focused and efficient analysis process [9, 25]. The beam path analysis algorithm [25] defines an efficient analysis pipeline that supports fast detection of particle beams (Figure 4 right). First, each time step is analyzed independently to detect individual particle bunches.…”