deepCR: Cosmic Ray Removal for Astronomical Imaging with Deep Learning
Channel: COSPAR2021 Workshops
Date: 02/02/2021

Authors: Keming Zhang, Joshua Bloom

Abstract: Astronomical imaging, in particular with space-based observatories, is frequently contaminated with artifacts arising from high energy cosmic ray (CR) particles affecting the detector. Here, we present deepCR, a deep learning based method for CR identification and replacement which was previously published as Zhang & Bloom (2020). We train and evaluate models on Hubble Space Telescope ACS/WFC images of sparse extragalactic fields, globular clusters, and resolved galaxies. At a false positive rate of 0.5%, deepCR achieves close to 100% detection rates in both extragalactic and globular cluster fields, and 91% in resolved galaxy fields, which is a significant improvement over the current state-of-the-art LACosmic. Compared to a parallel CPU implementation of LACosmic, deepCR CR mask predictions runs over one order-of-magnitude faster on GPU. For CR inpainting, the mean squared errors of deepCR predictions are 20 times lower in globular cluster fields, 5 times lower in resolved galaxy fields, and 3 times lower in extragalactic fields, compared to the best performing non-neural technique tested.