{"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/ps1-strm-neural-network-source-classification","title":"PS1-STRM: Neural network source classification and photometric redshift catalogue for PS1 $3\\pi$ DR1","arxiv_id":"1910.10167","date":"2019-10-22","proceeding":null,"authors":["Róbert Beck","István Szapudi","Heather Flewelling","Conrad Holmberg","Eugene Magnier"],"abstract":"The Pan-STARRS1 (PS1) $3\\pi$ survey is a comprehensive optical imaging survey of three quarters of the sky in the $grizy$ broad-band photometric filters. We present the methodology used in assembling the source classification and photometric redshift (photo-z) catalogue for PS1 $3\\pi$ Data Release 1, titled Pan-STARRS1 Source Types and Redshifts with Machine learning (PS1-STRM). For both main data products, we use neural network architectures, trained on a compilation of public spectroscopic measurements that has been cross-matched with PS1 sources. We quantify the parameter space coverage of our training data set, and flag extrapolation using self-organizing maps. We perform a Monte-Carlo sampling of the photometry to estimate photo-z uncertainty. The final catalogue contains $2,902,054,648$ objects. On our validation data set, for non-extrapolated sources, we achieve an overall classification accuracy of $98.1\\%$ for galaxies, $97.8\\%$ for stars, and $96.6\\%$ for quasars. Regarding the galaxy photo-z estimation, we attain an overall bias of $\\left<\\Delta z_{\\mathrm{norm}}\\right>=0.0005$, a standard deviation of $\\sigma(\\Delta z_{\\mathrm{norm}})=0.0322$, a median absolute deviation of $\\mathrm{MAD}(\\Delta z_{\\mathrm{norm}})=0.0161$, and an outlier fraction of $O=1.89\\%$. The catalogue will be made available as a high-level science product via the Mikulski Archive for Space Telescopes at https://doi.org/10.17909//t9-rnk7-gr88.","url_abs":"http://arxiv.org/abs/1910.10167v1","url_pdf":"http://arxiv.org/pdf/1910.10167v1.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":"ps1-strm-neural-network-source-classification","repo_url":"https://github.com/awe2/easy_photoz","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"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}