{"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/label-transfer-from-apogee-to-lamost-precise","title":"Label Transfer from APOGEE to LAMOST: Precise Stellar Parameters for 450,000 LAMOST Giants","arxiv_id":"1602.00303","date":"2016-01-31","proceeding":null,"authors":["Anna Y. Q. Ho","Melissa K. Ness","David W. Hogg","Hans-Walter Rix","Chao Liu","Fan Yang","Yong Zhang","Yonghui Hou","Yuefei Wang"],"abstract":"In this era of large-scale stellar spectroscopic surveys, measurements of stellar attributes (\"labels,\" i.e. parameters and abundances) must be made precise and consistent across surveys. Here, we demonstrate that this can be achieved by a data-driven approach to spectral modeling. With The Cannon, we transfer information from the APOGEE survey to determine precise Teff, log g, [Fe/H], and [$\\alpha$/M] from the spectra of 450,000 LAMOST giants. The Cannon fits a predictive model for LAMOST spectra using 9952 stars observed in common between the two surveys, taking five labels from APOGEE DR12 as ground truth: Teff, log g, [Fe/H], [\\alpha/M], and K-band extinction $A_k$. The model is then used to infer Teff, log g, [Fe/H], and [$\\alpha$/M] for 454,180 giants, 20% of the LAMOST DR2 stellar sample. These are the first [$\\alpha$/M] values for the full set of LAMOST giants, and the largest catalog of [$\\alpha$/M] for giant stars to date. Furthermore, these labels are by construction on the APOGEE label scale; for spectra with S/N > 50, cross-validation of the model yields typical uncertainties of 70K in Teff, 0.1 in log g, 0.1 in [Fe/H], and 0.04 in [$\\alpha$/M], values comparable to the broadly stated, conservative APOGEE DR12 uncertainties. Thus, by using \"label transfer\" to tie low-resolution (LAMOST R $\\sim$ 1800) spectra to the label scale of a much higher-resolution (APOGEE R $\\sim$ 22,500) survey, we substantially reduce the inconsistencies between labels measured by the individual survey pipelines. This demonstrates that label transfer with The Cannon can successfully bring different surveys onto the same physical scale.","url_abs":"http://arxiv.org/abs/1602.00303v4","url_pdf":"http://arxiv.org/pdf/1602.00303v4.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":"label-transfer-from-apogee-to-lamost-precise","repo_url":"https://github.com/annayqho/TheCannon","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","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}