Lithium has confused scientists for decades at almost every scale of the universe. Lithium-rich giants are peculiar stars with lithium abundances greater than model prediction. A large fraction of lithium-rich low-mass evolved stars are traditionally supposed to be red giant branch (RGB) stars. Recent studies, however, report that red clump (RC) stars are more frequent than RGB stars. Here, we present a uniquely large systematic study that combines direct asteroseismic analysis and spectroscopy of the lithium-rich stars. The majority of lithium-rich stars are confirmed to be RCs, whereas RGBs are a minority. We reveal that the distribution of lithium-rich RGBs declines steeply with increasing lithium abundance, with an upper limit of around 2.6 dex, whereas the lithium abundances of RCs extend to much higher values. We also find that the distributions of mass and nitrogen abundance are notably different between RC and RGB stars. These findings indicate that there is still an unknown process that significantly affects surface chemical composition in low-mass stellar evolution.
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The data that support the plots within this paper and other findings of this study are available from the corresponding authors upon reasonable request. Souce data are provided with this paper for Figs. 1–3. The LAMOST DR7 data are available for registered users at http://dr7.lamost.org. The stellar evolution tracks and isochrones data of PARSEC are available at https://people.sissa.it/sbressan/CAF09_V1.2S_M36_LT.
The codes that support the plots within this paper and other findings of this study are available from the corresponding authors upon reasonable request. The stellar evolution code MESA used to compute the HeWD–RGB merger is available at http://mesa.sourceforge.net. The code SPECTRUM used to compute the stellar templates for deriving the Li abundance for the low-resolution sample is available at http://www.appstate.edu/~grayro/spectrum/spectrum.html. The codes used to calculate the luminosity and surface gravity from the Gaia parallax are available at https://github.com/YutaoZhou/2020Na_codes. The codes involved in the plots are based on AstroLib, Coyote Library, Matplotlib, Pandas, NumPy and Astropy.
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This research is supported by the National Key R&D Program of China (grant no. 2019YFA0405502), the National Natural Science Foundation of China (grant nos. 11988101, 11833006, 11833002, 11890694, 11973052, 11973042, 11603037 and 11973049), and the Strategic Priority Research Program of the Chinese Academy of Sciences (CAS) (grant no. XDB34020205). We acknowledge support by the International Partnership Program of the CAS (grant no. 114A32KYSB20160049). H.-L.Y. acknowledges support from the Youth Innovation Promotion Association of the CAS (grant no. 2019060). T.M. is supported by a Grant-in-Aid for Fellows of the Japan Society for the Promotion of Science (JSPS) (grant no. 18J11326). K.P. acknowledges support from a Mt. Cuba Astronomical Foundation grant. This work is partially based on data collected at the Subaru telescope, which is operated by the National Astronomical Observatory of Japan, and was supported by a JSPS–CAS joint research programme. We acknowledge support from the staff at the Lijiang 2.4 m and 1.8 m telescopes, and support from the Telescope Access Program for accessing the Automated Planet Finder telescope. We acknowledge support from the Cultivation Project for LAMOST Scientific Payoff and Research Achievement of CAMS-CAS, and support from the Astronomical Big Data Joint Research Center, which is co-funded by the National Astronomical Observatories of the CAS and the Alibaba Cloud. The Guoshoujing telescope (LAMOST) is a National Major Scientific Project built by the CAS. Funding for the project has been provided by the National Development and Reform Commission. LAMOST is operated and managed by the National Astronomical Observatories of the CAS. We acknowledge the use of Gaia, APOGEE and Gaia-ESO data, and of the VizieR catalogue access tool. We also thank J.-J. Mao and X.-T. Fu for discussions.
The authors declare no competing interests.
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Martig et al. (2016) mapped the correlations46 of stellar masses with ASPCAP [N/Fe] using machine learning. Their ‘predicted’ masses of the common sources in our sample are plotted as the grey dots. The red and blue dots are Li-rich RC and RGB stars in our sample, respectively. The region marked with light blue is the most clumped mass range predicted by the HeWD-RGB merger model. For the predicted mass range of 0.8-1.8 M⊙ by the HeWD-RGB model, a fraction of stars indeed show much larger [N/Fe] (also see Fig. 3) compared to other RC stars within this mass range, but there seems no evident correlation between stars with high-mass and stars with high-[N/Fe].
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Yan, HL., Zhou, YT., Zhang, X. et al. Most lithium-rich low-mass evolved stars revealed as red clump stars by asteroseismology and spectroscopy. Nat Astron 5, 86–93 (2021). https://doi.org/10.1038/s41550-020-01217-8
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