Analyzing particle data plays an important role in many scientific applications such as fluid simulation, cosmology simulation and molecular dynamics. While there exist methods that can perform feature extraction and tracking for volumetric data, performing those tasks for particle data is more challenging because of the lack of explicit connectivity information. Although one may convert the particle data to volume first, this approach is at risk of incurring error and increasing the size of the data. In this paper, we take a deep learning approach to create feature representations for scientific particle data to assist feature extraction and tracking. We employ a deep learning model, which produces latent vectors to represent the relation between spatial locations and physical attributes in a local neighborhood. With the latent vectors, features can be extracted by clustering these vectors. To achieve fast feature tracking, the mean-shift tracking algorithm is applied in the feature space, which only requires inference of the latent vector for selected regions of interest. We validate our approach using two datasets and compare our method with other existing methods.
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