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.

deep CR 是一种基于深度学习的宇宙射线抑制算法，之前在哈勃太空望远镜 (HST) 高级巡天相机 (ACS)/WFC F606W 成像数据上被证明优于最先进的LACosmic 。在本研究报告中，我们提出了一个新的深度CR 型号用于 HST ACS/WFC 的所有过滤器。我们使用 ACS/WFC F435W、F606W 和 F814W 图像训练和测试模型，覆盖 ACS 光通道的整个光谱范围。全球模型显示，在河外场和球状星团中 CR 的检测率接近 100%，在已解析星系场中的检测率达到 91%。我们通过将其性能与同时训练的单过滤器模型进行比较，并通过对来自另一个以前未用于训练的过滤器的数据测试全局模型，进一步确认了该模型的全局适用性。