2019
DOI: 10.1093/mnras/stz2630
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Discovery of s-process enhanced stars in the LAMOST survey

Abstract: Here we present the discovery of 895 s-process-rich candidates from 454 180 giant stars observed by the Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST) using a data-driven approach. This sample constitutes the largest number of s-process enhanced stars ever discovered. Our sample includes 187 s-process-rich candidates that are enhanced in both barium and strontium, 49 stars with significant barium enhancement only and 659 stars that show only a strontium enhancement. Most of the stars in our… Show more

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Cited by 11 publications
(10 citation statements)
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“…Throughout this work, we will focus on the relatively hot A/F stars. The nature of the cooler Ba-enhanced stars will be explored in an upcoming study (M. Zhang et al 2020, in preparation; see also Norfolk et al 2019).…”
Section: Distribution In the T Effg Log Diagrammentioning
confidence: 99%
“…Throughout this work, we will focus on the relatively hot A/F stars. The nature of the cooler Ba-enhanced stars will be explored in an upcoming study (M. Zhang et al 2020, in preparation; see also Norfolk et al 2019).…”
Section: Distribution In the T Effg Log Diagrammentioning
confidence: 99%
“…Throughout this work, we will focus on the relatively hot A/F stars. The nature of the cooler Ba-enhanced stars will be explored in an upcoming study (Zhang et al in prep., see also Norfolk et al 2019).…”
Section: Distribution In the T Eff -Log G Diagrammentioning
confidence: 95%
“…Li et al (2018;hereafter L18) found 719 barium stars with strong spectral lines in Ba II at 4554 and Sr II at 4077 Å when they identified carbon stars from LAMOST DR4 by using a machine-learning method. Norfolk et al (2019;hereafter N19) reported 895 (out of 454,180 giants) barium giant candidates that were classified into 49 Ba-only, 659 Sr-only, and 49 both Sr-and Ba-enhancement stars from low-resolution spectra of LAMOST DR2. They predicted the stellar parameters and abundances (T eff , g log , [Fe/H], [α/M]) for their 454,180 giant samples based on a training sample that transfers labels from APOGEE to LAMOST by using the machine-learning method.…”
Section: Introductionmentioning
confidence: 99%