{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/using-convolutional-neural-networks-to","title":"Using convolutional neural networks to predict galaxy metallicity from three-color images","arxiv_id":"1810.12913","date":"2018-10-30","proceeding":null,"authors":["John F. Wu","Steven Boada"],"abstract":"We train a deep residual convolutional neural network (CNN) to predict the gas-phase metallicity ($Z$) of galaxies derived from spectroscopic information ($Z \\equiv 12 + \\log(\\rm O/H)$) using only three-band $gri$ images from the Sloan Digital Sky Survey. When trained and tested on $128 \\times 128$-pixel images, the root mean squared error (RMSE) of $Z_{\\rm pred} - Z_{\\rm true}$ is only 0.085 dex, vastly outperforming a trained random forest algorithm on the same data set (RMSE $=0.130$ dex). The amount of scatter in $Z_{\\rm pred} - Z_{\\rm true}$ decreases with increasing image resolution in an intuitive manner. We are able to use CNN-predicted $Z_{\\rm pred}$ and independently measured stellar masses to recover a mass-metallicity relation with $0.10$ dex scatter. Because our predicted MZR shows no more scatter than the empirical MZR, the difference between $Z_{\\rm pred}$ and $Z_{\\rm true}$ can not be due to purely random error. This suggests that the CNN has learned a representation of the gas-phase metallicity, from the optical imaging, beyond what is accessible with oxygen spectral lines.","url_abs":"http://arxiv.org/abs/1810.12913v1","url_pdf":"http://arxiv.org/pdf/1810.12913v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"using-convolutional-neural-networks-to","repo_url":"https://github.com/jwuphysics/galaxy-cnns","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1810.12913","atlas_url":"https://app.syntology.ai/?focus=1810.12913","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}