Sifting Through the Static: Moving Object Detection in Difference Images

TL;DR

This paper reports the detection of 99 Trans-Neptunian Objects using advanced image processing and neural network techniques, demonstrating improved capabilities for identifying faint moving objects in difference images, crucial for future large-scale surveys.

Contribution

The paper introduces extensions to the KBMOD platform, including a CNN stamp filter and GPU processing, enhancing moving object detection in difference images for large astronomical surveys.

Findings

Detected 75 new faint TNOs with VR magnitude ~25.

Recovered 24 known objects with established orbits.

Enhanced KBMOD with new filters and GPU processing for better detection.

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. Here we report the detection of 75 moving objects that we could not link to any other known objects, the faintest of which has a VR magnitude of $25.02 \pm 0.93$ using the KBMOD platform. We recover an additional 24 sources with previously-known orbits. We place constraints on the barycentric distance, inclination, and longitude of ascending node of these objects. The unidentified objects have a median barycentric distance of 41.28 au, placing them in the outer Solar System. The observed inclination and magnitude distribution of all detected objects is consistent with previously published KBO distributions. We describe extensions to KBMOD, including a robust percentile-based lightcurve filter, an in-line graphics processing unit (GPU) filter, new coadded stamp generation, and a convolutional neural network (CNN) stamp filter, which allow KBMOD to take advantage of difference images. These enchancements mark a significant improvement in the readiness of KBMOD for deployment on future big data surveys such as LSST.