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Identification of Blue Horizontal-Branch Stars From LAMOST DR5

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arxiv 2311.16430 v1 pith:UWSTCQJ5 submitted 2023-11-28 astro-ph.GA astro-ph.SR

classification astro-ph.GAastro-ph.SR
keywords starslamostlinebluecataloggalactichorizontal-branchlarge
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abstract

We construct a new catalog of the blue horizontal-branch (BHB) stars from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) DR5 dataset, which contains 5355+81 BHB stars at high Galactic latitude (($|Glat|>20^{\circ}$). We combine the spectral line indices with a set of Balmer line profile selection criteria to identify the BHB stars. During the selection process, we use the line index of \ion{Ca}{2}\,K to exclude the metal-rich A-type dwarfs. We obtain their atmospheric parameters by cross-matching our BHB stars with the catalog provided by \citet{Xiang2022}. The results show that our sample is consistent with the theoretical $T_{\rm eff}$-log\,$g$ evolutionary tracks of the BHB stars, indicating that our method is robust for identifying BHB stars from the LAMOST spectra. Their spatial distribution indicates that most of our BHB stars are located in the inner halo or the disk of the Milky Way. Combined with other BHB samples from the literature, the BHB stars can cover a large Galactic volume, which makes it a better probe for studying the kinematics, dynamics, and structural characteristics of the Milky Way.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Helium-shell Burning Blue Horizontal Branch Star Produced from Common Envelope Evolution

    astro-ph.SR 2026-08 conditional novelty 6.0 of 10

    Feige 64 is a 0.35 solar mass helium-shell-burning BHB star in a 0.826-day orbit with a 1.26 solar mass white dwarf, best explained as a common-envelope remnant.

  2. Phys4D: Fine-Grained Physics-Consistent 4D Modeling from Video Diffusion

    cs.CV 2026-03 conditional novelty 6.0 of 10

    Phys4D improves video diffusion models' physical consistency via pseudo-supervised depth/motion heads, simulation-grounded fine-tuning, and RL with a 4D Chamfer reward.

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