{"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/a-morphological-model-to-separate-resolved","title":"A Morphological Model to Separate Resolved--unresolved Sources in the DESI Legacy Surveys: Application in the LS4 Alert Stream","arxiv_id":"2505.17174","date":"2025-05-22","proceeding":null,"authors":["Chang Liu","Adam A. Miller","Joshua S. Bloom","Robert A. Knop","Peter E. Nugent"],"abstract":"Separating resolved and unresolved sources in large imaging surveys is a fundamental step to enable downstream science, such as searching for extragalactic transients in wide-field time-domain surveys. Here we present our method to effectively separate point sources from the resolved, extended sources in the Dark Energy Spectroscopic Instrument (DESI) Legacy Surveys (LS). We develop a supervised machine-learning model based on the Gradient Boosting algorithm $\\texttt{XGBoost}$. The features input to the model are purely morphological and are derived from the tabulated LS data products. We train the model using $\\sim$$2\\times10^5$ LS sources in the COSMOS field with HST morphological labels and evaluate the model performance on LS sources with spectroscopic classification from the DESI Data Release 1 ($\\sim$$2\\times10^7$ objects) and the Sloan Digital Sky Survey Data Release 17 ($\\sim$$3\\times10^6$ objects), as well as on $\\sim$$2\\times10^8$ Gaia stars. A significant fraction of LS sources are not observed in every LS filter, and we therefore build a ''Hybrid'' model as a linear combination of two \\texttt{XGBoost} models, each containing features combining aperture flux measurements from the ''blue'' ($gr$) and ''red'' ($iz$) filters. The Hybrid model shows a reasonable balance between sensitivity and robustness, and achieves higher accuracy and flexibility compared to the LS morphological typing. With the Hybrid model, we provide classification scores for $\\sim$$3\\times10^9$ LS sources, making this the largest ever machine-learning catalog separating resolved and unresolved sources. The catalog has been incorporated into the real-time pipeline of the La Silla Schmidt Southern Survey (LS4), enabling the identification of extragalactic transients within the LS4 alert stream.","url_abs":"https://arxiv.org/abs/2505.17174v1","url_pdf":"https://arxiv.org/pdf/2505.17174v1.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":"a-morphological-model-to-separate-resolved","repo_url":"https://github.com/slowdivePTG/LS-PSC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}