Physical Sciences and Mathematics Commons^™

A Machine Learning Approach To The Detection Of Ghosting And Scattered Light Artifacts In Dark Energy Survey Images, C. Chang, A. Drlica-Wagner, S. M. Kent, B. Nord, D. M. Wang, M.H.L.S. Wang

Student Publications & Research

Astronomical images are often plagued by unwanted artifacts that arise from a number of sources including imperfect optics, faulty image sensors, cosmic ray hits, and even airplanes and artificial satellites. Spurious reflections (known as “ghosts”) and the scattering of light off the surfaces of a camera and/or telescope are particularly difficult to avoid. Detecting ghosts and scattered light efficiently in large cosmological surveys that will acquire petabytes of data can be a daunting task. In this paper, we use data from the Dark Energy Survey to develop, train, and validate a machine learning model to detect ghosts and scattered light …