Sifting Through the Static: Exploring the Space Beyond Neptune with Digital Tracking

Abstract

Trans-Neptunian Objects (TNOs) provide a window into the history of the Solar System, but they can be challenging to observe due to their distance from the Sun and relatively low brightness. Digital tracking helps address these challenges by algorithmically searching many possible TNO trajectories in a stack of images, enabling detection of TNOs too faint to detectin single images. Here we report the detection and characterization of 86 classical TNOs, 5 detached TNOs, 6 resonant TNOs, and 2 scattering TNOs that we could not link to any other known objects. We report measurements of semi-major axis, eccentricity, inclination, longitude of ascending node, argument of pericenter, and time of pericenter passage for these 99 objects. We also report values for the absolute magnitude H in the VR band with a largest measured H value of H=9.63. These objects are dynamically classified using 10 Myr Rebound orbital integrations. Additionally, we report the detection of 75 moving objects with short ~4 day arcs that we could not link to any other known objects and place constraints on the barycentric distance, inclination, and longitude of ascending node of these objects. We describe extensions to the Kernel-Based Moving Object Detection (KBMOD) software that helped enable these detections, including an in-line graphics processing unit (GPU) filter, a convolutional neural network (CNN) stamp filter, and an astrometric and photometric post-processing tool. These tools enable KBMOD to take advantage of difference images and help ready KBMOD for deployment on future big data surveys such as LSST.