KBMOD: Kernel Based Moving Object Detection

Searching for faint Solar System objects Kuiper Belt Objects (KBOs) are a population of Solar System objects that exist beyond the orbit of Neptune. Finding these objects is important because understanding their true distribution teaches us about the formation history of the Solar System and especially about the evolution of the orbits of the gas giants. However, due to the large distances from the Earth and Sun KBOs are very faint and hard to find. Some existing techniques for finding moving objects rely on the objects being bright enough for observation in a single image, but here at DIRAC we are working on a type of technique based upon “shift-and-stack” algorithms.

Find more information in the following papers [Gladman & Kavelaars 1997, Kuiper Belt searches from the Palomar 5-m telescope]; [Allen et al. 2001, The Edge of the Solar System]; [Bernstein et al. 2004, The Size Distribution of Trans-Neptunian Bodies].

“Shift-and-stack” techniques are able to find objects that are fainter than those that can be found in a single image by shifting multiple images of the same part of the sky along the path of a potential orbit and adding up the light from any moving objects following that orbit. Static objects blur out while potential moving objects pop out as a point source as in the figure below from our paper [Whidden et al. 2019, Fast Algorithms for Slow Moving Asteroids: Constraints on the Distribution of Kuiper Belt Objects].

Shift and Stack Example

Digital Tracking This “shift-and-stack” method originally worked by shifting along a limited number of trajectories on the order of a few dozen and then looking by eye for point sources in the resulting stacks. More recently, astronomers developed “digital tracking” where we use computers to search many more possible trajectories and find point sources in large stacks of data. As we move to larger stacks of images and longer baselines in our search we are able to find fainter and slower moving objects but this also creates challenges as our search parameter space becomes much larger as we go further in time. This is because we want to find the slowest objects without missing faster ones and must search a much larger group of possible orbits. To help solve this problem we developed our technique Kernel Based Moving Object Detection (KBMOD).

KBMOD To tackle the problem of searching on the order of 10<sup>12</sup> possible orbits we have turned to using Graphics Processing Units (GPUs). GPUs are much better suited to highly parallel applications than traditional CPUs and since we are performing the same operation of adding up the flux values along trillions of trajectories repeatedly the GPU is perfect for our algorithm. In fact, KBMOD is capable of searching on the order of 10<sup>10</sup> trajectories in a stack of 10-15 4k-by-4k images in a minute using a consumer grade GPU. The software is up and running and in our first application of KBMOD to the 2015 HITS dataset [Förster et al. 2016, The High Cadence Transient Survey (HITS). I. Survey Design and Supernova Shock Breakout Constraints] we discovered 39 new KBOs that were reported to the Minor Planet Center as well as recovering 6 previously reported objects. Further development of KBMOD is ongoing and we are applying it to new and different datasets.

To follow along with our progress stay tuned to our GitHub Repository.