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Published November 1, 2017 | Submitted + Published
Journal Article Open

Measuring 14 Elemental Abundances with R = 1800 LAMOST Spectra

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.

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.

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Published - Ting_2017_ApJL_849_L9.pdf

Submitted - 1708.01758.pdf

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Additional details

Created:
August 19, 2023
Modified:
October 17, 2023