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Sifting through the Static: Moving Object Detection in Difference Images

  • Hayden Smotherman
  • , Andrew J. Connolly
  • , J. Bryce Kalmbach
  • , Stephen K.N. Portillo
  • , Dino Bektesevic
  • , Siegfried Eggl
  • , Mario Juric
  • , Joachim Moeyens
  • , Peter J. Whidden

Research output: Contribution to journalArticlepeer-review

Abstract

Trans-Neptunian objects 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 0.93 using the Kernel-Based Moving Object Detection (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 filter, new coadded stamp generation, and a convolutional neural network stamp filter, which allow KBMOD to take advantage of difference images. These enhancements mark a significant improvement in the readiness of KBMOD for deployment on future big data surveys such as LSST.

Original languageEnglish (US)
Article number245
JournalAstronomical Journal
Volume162
Issue number6
Early online dateNov 17 2021
DOIs
StatePublished - Dec 1 2021
Externally publishedYes

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science

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