Finding moving objects below the single image detection limit

Title: Finding moving objects below the single-image detection limit We present new results from the Kernel Based Moving Object Detection (KBMOD) pipeline, which detects moving objects that would be too dim to be detected in a single image. This pipeline takes a series of images of a given field, creates matricies of the likelihood of a source detection in a given pixel, and uses GPU-accelerated computing to" shift-and-stack" these likelihood matricies along a grid of position and velocity vectors, returning stacked maximum likelihood results over a given threshold. We then use a lightcurve filter followed by a image central moment filter to help eliminate false positives. Unlike traditional linking algorithms which require source detections in a given image before linking can occur, KBMOD does not require a source detection in any individual image in a given field. Furthermore, KBMOD does not need any fixed image …