Details

Autor(en) / Beteiligte

• Kwon, K. J.
• Zhang, Keming
• Bloom, Joshua S.

Titel

deepCR on ACS/WFC: Cosmic-Ray Rejection for HST ACS/WFC Photometry

Ist Teil von

• Research notes of the AAS, 2021-04, Vol.5 (4), p.98

Erscheinungsjahr

2021

Beschreibungen/Notizen

• Abstract deep CR 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 deep CR 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.