{"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/estimation-of-photometric-redshifts-ii","title":"Estimation of Photometric Redshifts. II. Identification of Out-of-Distribution Data with Neural Networks","arxiv_id":"2112.07104","date":"2021-12-14","proceeding":null,"authors":["Joongoo Lee","Min-Su Shin"],"abstract":"In this study, we propose a three-stage training approach of neural networks for both photometric redshift estimation of galaxies and detection of out-of-distribution (OOD) objects. Our approach comprises supervised and unsupervised learning, which enables using unlabeled (UL) data for OOD detection in training the networks. Employing the UL data, which is the dataset most similar to the real-world data, ensures a reliable usage of the trained model in practice. We quantitatively assess the model performance of photometric redshift estimation and OOD detection using in-distribution (ID) galaxies and labeled OOD (LOOD) samples such as stars and quasars. Our model successfully produces photometric redshifts matched with spectroscopic redshifts for the ID samples and identifies well the LOOD objects with more than 98% accuracy. Although quantitative assessment with the UL samples is impracticable due to the lack of labels and spectroscopic redshifts, we also find that our model successfully estimates reasonable photometric redshifts for ID-like UL samples and filter OOD-like UL objects. The code for the model implementation is available at https://github.com/GooLee0123/MBRNN_OOD.","url_abs":"https://arxiv.org/abs/2112.07104v1","url_pdf":"https://arxiv.org/pdf/2112.07104v1.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":"estimation-of-photometric-redshifts-ii","repo_url":"https://github.com/goolee0123/mbrnn_ood","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","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}