Abstract
Abstract deepCR is a deep-learning-based cosmic-ray rejection algorithm previously demonstrated to be superior to state-of-the-art LACosmic on Hubble Space Telescope (HST) Advanced Camera for Surveys (ACS)/WFC F606W imaging data. In this research note, we present a new deepCR model for use on all filters of HST ACS/WFC. We train and test the model with ACS/WFC F435W, F606W, and F814W images, covering the entire spectral range of the ACS optical channel. The global model demonstrates near 100% detection rates of CRs in extragalactic fields and globular clusters and 91% in resolved galaxy fields. We further confirm the global applicability of the model by comparing its performance against single-filter models that were trained simultaneously and by testing the global model on data from another filter which was not previously used for training.
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