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Wu's research group and collaborators have already exploited survey imaging data attempted to pick up the slack on better characterizing galaxies' morphologies, and have done so using large imaging surveys in order to study a variety of topics related to galaxy evolution. These include estimating galaxy gas-phase metallicities (Wu & Boada 2019), predicting neutral hydrogen gas content (Wu 2020), separating AGN and star-forming systems (Holwerda, Wu, et al. 2021; Guo, Wu, & Sharon 2022), identifying rare nearby dwarf satellite galaxies (Wu et al. 2022; Darragh-Ford, Wu et al. in prep), and even predicting the entire optical spectrum of galaxies (Wu & Peek 2020, see above). In each case, deep convolutional neural networks play a key role in representing the morphological information found in galaxy images.

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