Presentation #304.02 in the session Computation, Data Handling, Image Analysis.
We present a new approach to identify satellite trails (and other linear artifacts) in ACS/WFC imaging data using a modified Radon Transform. Our approach is sensitive to features with mean brightness significantly below the background noise level, and it is resistant to the influence of bright astronomical sources (e.g., stars and galaxies) in most cases. Comparing with a set of 358 images with satellite trails identified by eye, we find a trail recovery rate of 85% and a false detection rate (after removing diffraction spikes that are easily filtered) of 2.5%. By performing an analysis using a much larger ACS/WFC data set where false trails are identified by their persistence across multiple images of the same field (e.g., cases of scattered light artifacts or chance persistent alignments of stars/galaxies) we identify the modified Radon Transform parameter space and image properties where our algorithm is unreliable (mostly image corners and extremely dense fields), and estimate a false detection rate of ~10% elsewhere. We apply our method to ~30,000 ACS/WFC images taken between 2002 and 2022 and find the rate of satellite trail contamination has increased by approximately a factor of two in the last two decades, with ~9% of images currently affected, but there is no clear systematic evolution in the typical trail brightness. Our new satellite trail identification program is available for public use as part of the acstools package.