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Measuring 14 Elemental Abundances with R = 1800 LAMOST Spectra

Ting, Yuan-Sen and Rix, Hans-Walter and Conroy, Charlie and Ho, Anna Y. Q. and Lin, Jane (2017) Measuring 14 Elemental Abundances with R = 1800 LAMOST Spectra. Astrophysical Journal Letters, 849 (1). Art. No. L9. ISSN 2041-8213. https://resolver.caltech.edu/CaltechAUTHORS:20171024-140459353

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Abstract

The LAMOST survey has acquired low-resolution spectra (R = 1800) for 5 million stars across the Milky Way, far more than any current stellar survey at a corresponding or higher spectral resolution. It is often assumed that only very few elemental abundances can be measured from such low-resolution spectra, limiting their utility for Galactic archaeology studies. However, Ting et al. used ab initio models to argue that low-resolution spectra should enable precision measurements of many elemental abundances, at least in theory. Here, we verify this claim in practice by measuring the relative abundances of 14 elements from LAMOST spectra with a precision of ≾0.1 dex for objects with S/N_(LAMOST) ≳ 30 (per pixel). We employ a spectral modeling method in which a data-driven model is combined with priors that the model gradient spectra should resemble ab initio spectral models. This approach assures that the data-driven abundance determinations draw on physically sensible features in the spectrum in their predictions and do not just exploit astrophysical correlations among abundances. Our analysis is constrained to the number of elemental abundances measured in the APOGEE survey, which is the source of the training labels. Obtaining high quality/resolution spectra for a subset of LAMOST stars to measure more elemental abundances as training labels and then applying this method to the full LAMOST catalog will provide a sample with more than 20 elemental abundances, which is an order of magnitude larger than current high-resolution surveys, substantially increasing the sample size for Galactic archaeology.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.3847/2041-8213/aa921cDOIArticle
http://iopscience.iop.org/article/10.3847/2041-8213/aa921c/metaPublisherArticle
https://arxiv.org/abs/1708.01758arXivDiscussion Paper
ORCID:
AuthorORCID
Ting, Yuan-Sen0000-0001-5082-9536
Rix, Hans-Walter0000-0003-4996-9069
Conroy, Charlie0000-0002-1590-8551
Ho, Anna Y. Q.0000-0002-9017-3567
Additional Information:© 2017 The American Astronomical Society. Received 2017 August 5; revised 2017 October 6; accepted 2017 October 8; published 2017 October 23. Y.S.T is supported by the Australian Research Council Discovery Program DP160103747, the Carnegie-Princeton Fellowship, and the Martin A. and Helen Chooljian Membership from the Institute for Advanced Study at Princeton. H.W.R.'s research contribution is supported by the European Research Council under the European Union's Seventh Framework Programme (FP 7) ERC Grant Agreement n. [321035] and by the DFG's SFB-881 (A3) Program. C.C. acknowledges support from NASA grant NNX13AI46G, NSF grant AST-1313280, and the Packard Foundation. A.Y.Q.H. is supported by a National Science Foundation Graduate Research Fellowship under grant No. DGE1144469.
Funders:
Funding AgencyGrant Number
Australian Research CouncilDP160103747
Carnegie-Princeton FellowshipUNSPECIFIED
Institute for Advanced StudyUNSPECIFIED
European Research Council (ERC)321035
Deutsche Forschungsgemeinschaft (DFG)SFB-881
NASANNX13AI46G
NSFAST-1313280
David and Lucile Packard FoundationUNSPECIFIED
NSF Graduate Research FellowshipDGE-1144469
Subject Keywords:methods: data analysis – stars: abundances
Issue or Number:1
Record Number:CaltechAUTHORS:20171024-140459353
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20171024-140459353
Official Citation:Yuan-Sen Ting et al 2017 ApJL 849 L9
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:82623
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:24 Oct 2017 21:13
Last Modified:03 Oct 2019 18:56

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