Efficient Search and Detection of Faint Moving Objects in Image Data

Abstract

Abstract The search and detection of faint moving objects in image data can enable discoveries of small solar system bodies. To detect objects fainter than the single-frame sensitivity limit, track-before-detect methods can improve the signal-to-noise ratio of the object of interest by incoherently adding the object’s signal across multiple frames. However, traditional track-before-detect techniques can become computationally intensive over large search volumes. In this work, we present a computational approach to significantly speed up the search process by applying dynamic-programming techniques to implement the discrete X-ray transform. In this approach, image frames are processed in stages, in each of which pairs of frames are shifted and added to generate short-track segments, which are combined in later stages to form longer tracks. The algorithm speedup comes from the fact that a single short track segment can be reused multiple times for different longer tracks without the need for recomputing. Benchmark testing with simulated data shows that the method presented in this paper results in a significant reduction in runtime in comparison to a traditional track-before-detect approach. As a proof of concept, we demonstrated the applicability of the technique in performing a blind search for faint asteroids in image data collected from the Transiting Exoplanet Survey Satellite, leading to the detection of more than a thousand asteroids below the single-frame detection limit with moderate computational resources. The approach presented in this work has the potential to enable efficient discovery of previously undetected faint solar system objects across multiple orbit classes.