Masses and Ages for 230,000 LAMOST Giants, via Their Carbon and Nitrogen Abundances

Creators: Ho, Anna Y. Q.; Rix, Hans-Walter; Ness, Melissa K.; Hogg, David W.; Liu, Chao; Ting, Yuan-Sen

Abstract

We measure carbon and nitrogen abundances to a precision of ≾0.1 dex for 450,000 giant stars from their low-resolution (R ~ 1800) LAMOST DR2 survey spectra. We use these [C/M] and [N/M] measurements, together with empirical relations based on the APOKASC sample, to infer stellar masses and implied ages for 230,000 of these objects to 0.08 dex and 0.2 dex respectively. We use The Cannon, a data-driven approach to spectral modeling, to construct a predictive model for LAMOST spectra. Our reference set comprises 8125 stars observed in common between the APOGEE and LAMOST surveys, taking seven APOGEE DR12 labels (parameters) as ground truth: T_(eff), log g, [M/H], [α/M], [C/M], [N/M], and A_k. We add seven colors to the Cannon model, based on the g, r, i, J, H, K, W1, W2 magnitudes from APASS, 2MASS, and WISE, which improves our constraints on T_(eff) and log g by up to 20% and on A_k by up to 70%. Cross-validation of the model demonstrates that, for high-S/N objects, our inferred labels agree with the APOGEE values to within 50 K in temperature, 0.04 mag in A_k, and <0.1 dex in log g, [M/H], [C/M], [N/M], and [α/M]. We apply the model to 450,000 giants in LAMOST DR2 that have not been observed by APOGEE. This demonstrates that precise individual abundances can be measured from low-resolution spectra and represents the largest catalog to date of homogeneous stellar [C/M], [N/M], masses, and ages. As a result, we greatly increase the number and sky coverage of stars with mass and age estimates.

Additional Information

© 2017 The American Astronomical Society. Received 2016 September 10; revised 2017 April 4; accepted 2017 April 12; published 2017 May 22. We are grateful to Evan Kirby (Caltech) for his careful reading and constructive feedback on the manuscript. It is furthermore a pleasure to thank Jon Bird (Vanderbilt), Sven Buder (MPIA), Andy Casey (IoA Cambridge), Christina Eilers (MPIA), Jim Fuller (Caltech), Lynne Hillenbrand (Caltech), Sarah Martell (UNSW), and Marie Martig (MPIA) for helpful discussions and assistance. A.Y.Q.H. is grateful to the community at the MPIA for their support and hospitality during the period in which much of this work was performed. H.W.R. acknowledges support of the Miller Institute at UC Berkeley through a visiting professorship during the completion of this work. Finally, we thank the anonymous referee whose detailed comments greatly improved the clarity and precision of the paper. A.Y.Q.H. was supported by a National Science Foundation Graduate Research Fellowship under grant No. DGE1144469. M.K.N. and H.W.R. have received funding for this research from the European Research Council under the European Union's Seventh Framework Programme (FP 7) ERC Grant Agreement No. [321035]. D.W.H. was partially supported by the NSF (grant IIS-1124794), NASA (grant NNX08AJ48G), and the Moore-Sloan Data Science Environment at NYU. C.L. acknowledges the Strategic Priority Research Program "The Emergence of Cosmological Structures" of the Chinese Academy of Sciences, grant No. XDB09000000, the National Key Basic Research Program of China 2014CB845700, and the National Natural Science Foundation of China (NSFC) grant No. 11373032 and 11333003. Guoshoujing Telescope (the Large Sky Area Multi-Object Fiber Spectroscopic Telescope LAMOST) is a National Major Scientific Project built by the Chinese Academy of Sciences. Funding for the project has been provided by the National Development and Reform Commission. LAMOST is operated and managed by the National Astronomical Observatories, Chinese Academy of Sciences. Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions. SDSS-IV acknowledges support and resources from the Center for High-Performance Computing at the University of Utah. The SDSS web site is www.sdss.org. SDSS-IV is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration including the Brazilian Participation Group, the Carnegie Institution for Science, Carnegie Mellon University, the Chilean Participation Group, the French Participation Group, Harvard-Smithsonian Center for Astrophysics, Instituto de Astrofísica de Canarias, The Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo, Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical Observatory of China, New Mexico State University, New York University, University of Notre Dame, Observatário Nacional/MCTI, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autónoma de México, University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University, and Yale University. This research has made use of the APASS database, located at the AAVSO web site. Funding for APASS has been provided by the Robert Martin Ayers Sciences Fund. This publication makes use of data products from the Two Micron All Sky Survey, which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center/California Institute of Technology, funded by the National Aeronautics and Space Administration and the National Science Foundation. Facilities: Sloan (APOGEE spectrograph), LAMOST, WISE, AAVSO.

Attached Files

Published - Ho_2017_ApJ_841_40.pdf

Submitted - 1609.03195v1.pdf

Files

Ho_2017_ApJ_841_40.pdf

Files (4.2 MB)

Name	Size	Download all
Ho_2017_ApJ_841_40.pdf md5:e7a84a7a38e4c5875688e96bd261182c	3.2 MB	Preview Download
1609.03195v1.pdf md5:26f43e680b045855cd8b088082e69de7	971.8 kB	Preview Download

Additional details

	All versions	This version
Views	0	0
Downloads	0	0
Data volume	0 Bytes	0 Bytes