{"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/photometric-fe-h-of-rrab-stars-in-the-g-and-k","title":"Photometric [Fe/H] of RRab stars in the $G$ and $K_s$ bands by deep learning","arxiv_id":"2205.14471","date":"2022-05-28","proceeding":null,"authors":["István Dékány","Eva K. Grebel"],"abstract":"RR Lyrae stars are useful chemical tracers thanks to the empirical relationship between their heavy-element abundance and the shape of their light curves. However, the consistent and accurate calibration of this relation across multiple photometric wavebands has been lacking. We have devised a new method for the metallicity estimation of fundamental-mode RR Lyrae stars in the Gaia optical $G$ and near-infrared VISTA $K_s$ wavebands by deep learning. First, an existing metallicity prediction method is applied to large photometric data sets, which are then used to train long short-term memory recurrent neural networks for the regression of the [Fe/H] to the light curves in other wavebands. This approach allows an unbiased transfer of our accurate, spectroscopically calibrated $I$-band formula to additional bands at the expense of minimal additional noise. We achieve a low mean absolute error of $0.1$ dex and high $R^2$ regression performance of $0.84$ and $0.93$ for the $K_s$ and $G$ bands, respectively, measured by cross-validation. The resulting predictive models are deployed on the Gaia DR2 and VVV inner-bulge RR Lyrae catalogs. We estimate mean metallicities of $-1.35$ dex for the inner bulge and $-1.7$ for the halo, which are significantly less than values obtained by earlier photometric prediction methods. Using our results, we establish a public catalog of photometric metallicities of over 60,000 Galactic RR Lyrae stars, and provide an all-sky map of the resulting RR Lyrae metallicity distribution. The software code used for training and deploying our recurrent neural networks is made publicly available in the open-source domain.","url_abs":"https://arxiv.org/abs/2205.14471v1","url_pdf":"https://arxiv.org/pdf/2205.14471v1.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":"photometric-fe-h-of-rrab-stars-in-the-g-and-k","repo_url":"https://github.com/idekany/lcfit","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"photometric-fe-h-of-rrab-stars-in-the-g-and-k","repo_url":"https://github.com/idekany/rrl_feh_nn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}