An Algorithm for the Visualization of Relevant Patterns in Astronomical Light Curves
This provides astronomers with an intuitive tool for pattern recognition in complex time series data, though it is incremental as it adapts existing dictionary-based techniques to a new domain.
The paper tackles the problem of visualizing significant patterns in astronomical light curves by proposing a dictionary-based method that encodes and reconstructs these patterns, tested on OGLE-III and StarLight databases to automatically highlight features like local peaks and drops.
Within the last years, the classification of variable stars with Machine Learning has become a mainstream area of research. Recently, visualization of time series is attracting more attention in data science as a tool to visually help scientists to recognize significant patterns in complex dynamics. Within the Machine Learning literature, dictionary-based methods have been widely used to encode relevant parts of image data. These methods intrinsically assign a degree of importance to patches in pictures, according to their contribution in the image reconstruction. Inspired by dictionary-based techniques, we present an approach that naturally provides the visualization of salient parts in astronomical light curves, making the analogy between image patches and relevant pieces in time series. Our approach encodes the most meaningful patterns such that we can approximately reconstruct light curves by just using the encoded information. We test our method in light curves from the OGLE-III and StarLight databases. Our results show that the proposed model delivers an automatic and intuitive visualization of relevant light curve parts, such as local peaks and drops in magnitude.