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ABSTRACT Very metal-poor stars ($$\rm [Fe/H] \lt -2$$) in the Milky Way are fossil records of early chemical evolution and the assembly and structure of the Galaxy. However, they are rare and hard to find. Gaia DR3 has provided over 200 million low-resolution (R ≈ 50) XP spectra, which provides an opportunity to greatly increase the number of candidate metal-poor stars. In this work, we utilize the XGBoost classification algorithm to identify ∼200 000 very metal-poor star candidates. Compared to past work, we increase the candidate metal-poor sample by about an order of magnitude, with comparable or better purity than past studies. First, we develop three classifiers for bright stars (BP < 16). They are Classifier-T (for Turn-off stars), Classifier-GC (for Giant stars with high completeness), and Classifier-GP (for Giant stars with high purity) with expected purity of 52 per cent/45 per cent/76 per cent and completeness of 32 per cent/93 per cent/66 per cent, respectively. These three classifiers obtained a total of 11 000/111 000/44 000 bright metal-poor candidates. We apply model-T and model-GP on faint stars (BP > 16) and obtain 38 000/41 000 additional metal-poor candidates with purity 29 per cent/52 per cent, respectively. We make our metal-poor star catalogues publicly available, for further exploration of the metal-poor Milky Way.
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From the inner to outer Milky Way: a photometric sample of 2.6 million red clump stars
ABSTRACT Large pristine samples of red clump stars are highly sought after given that they are standard candles and give precise distances even at large distances. However, it is difficult to cleanly select red clumps stars because they can have the same Teff and log g as red giant branch stars. Recently, it was shown that the asteroseismic parameters, $$\rm {\Delta }$$P and $$\rm {\Delta \nu }$$, which are used to accurately select red clump stars, can be derived from spectra using the change in the surface carbon to nitrogen ratio ([C/N]) caused by mixing during the red giant branch. This change in [C/N] can also impact the spectral energy distribution. In this study, we predict the $$\rm {\Delta }$$P, $$\rm {\Delta \nu }$$, Teff, and log g using 2MASS, AllWISE, Gaia, and Pan-STARRS data in order to select a clean sample of red clump stars. We achieve a contamination rate of ∼20 per cent, equivalent to what is achieved when selecting from Teff and log g derived from low-resolution spectra. Finally, we present two red clump samples. One sample has a contamination rate of ∼20 per cent and ∼405 000 red clump stars. The other has a contamination of ∼33 per cent and ∼2.6 million red clump stars that includes ∼75 000 stars at distances >10 kpc. For |b| > 30 deg, we find ∼15 000 stars with contamination rate of ∼9 per cent. The scientific potential of this catalogue for studying the structure and formation history of the Galaxy is vast, given that it includes millions of precise distances to stars in the inner bulge and distant halo where astrometric distances are imprecise.
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- Award ID(s):
- 1907417
- PAR ID:
- 10183015
- Date Published:
- Journal Name:
- Monthly Notices of the Royal Astronomical Society
- Volume:
- 495
- Issue:
- 3
- ISSN:
- 0035-8711
- Page Range / eLocation ID:
- 3087 to 3103
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/mnras/staa1226
