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. There is a pressing need for computational methods that aid in 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. To address this challenge we describe in this poster 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. By combining the automatic feature detection with a novel visualization tool for fast, intuitive, query-based exploration of acceleration features, we enable 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 study the formation and evolution of particle beams using simulations modeling different plasma-based accelerator designs.