The GOGREEN Survey: AI Powered Deconvolution Lifts The Veil on Outside-in Environmental Quenching at z > 1

Abstract

A powerful probe of the physical processes that quench star formation in dense environments is determining where within galaxies star formation is suppressed. At high redshift, the spatial resolution of multi-band imaging limits such measurements. We use deep-learning-based deconvolution to recover spatially resolved optical and near-infrared photometry for galaxies in nine GOGREEN clusters at 1<z<1.4, using customized models trained on HST and JWST imaging. Using resolved rest-frame UVJ colors, we classify galaxies by the star-forming states of their inner and outer regions into predominantly star-forming, predominantly quiescent, inside-quenched, or outside-quenched. We find that 24% of galaxies classified as quiescent from their integrated colors retain significant star formation. The predominantly quiescent fraction increases with stellar mass and is higher in clusters than in the field while the cluster quenched fraction excess is, when limiting to predominantly quenched galaxies, approximately 20%. Contrary to previous GOGREEN studies using integrated colors, we find this excess to be independent of stellar mass, demonstrating that partially quenched galaxies can bias measurements based on integrated colors. Among galaxies retaining significant star formation, outside-quenched galaxies are substantially more common than inside-quenched galaxies and have a fraction excess of (22.8+/-5.8)% in clusters relative to the field at low masses. This provides evidence that clusters preferentially suppress star formation in the outskirts of low-mass galaxies. Our results demonstrate the importance of spatially resolved classifications for interpreting environmental quenching at z~1 and the potential of deep-learning-based deconvolution to recover such information from large ground-based imaging datasets.