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Abstract The origin of very metal-poor (VMP; [Fe/H] ≤ −2.0) stars on planar orbits has been the subject of great attention since their first discovery. However, prior to the release of the Gaia BP/RP (XP) spectra and large photometric samples such as SkyMapper, SAGES, J-PLUS, and S-PLUS, most studies have been limited due to their small sample sizes or strong selection effects. Here, we crossmatch photometric metallicities derived from Gaia XP synthetic photometry and geometric distances from Bailer-Jones et al., and select 12,000 VMP stars (1604 dwarfs and 10,396 giants) with available high-quality astrometry. After calculating dynamical parameter estimates usingAGAMA, we employ the nonnegative matrix factorization technique to thev_ϕdistribution across bins in $Z_{\max }$(the maximum height above or below the Galactic plane during the stellar orbit). We find three primary populations of the selected VMP stars: halo, disk system, and the Gaia Sausage/Enceladus structure. The fraction of disk-like stars decreases with increasing $Z_{\max }$(as expected), although it is still ∼20% for stars with $Z_{\max }$∼ 3 kpc. Similar results emerge from the application of the Hayden criterion, which separates stellar populations on the basis of their orbital inclination angles relative to the Galactic plane. We argue that such high fractions of disk-like stars indicate that they are an independent component, rather than originating solely from Galactic building blocks or heating by minor mergers. We suggest that most of these VMP stars are members of the hypothesized “primordial” disk. 

Abstract We apply the stellar locus method to synthetic (BP–RP)_XPSPand (BP–G)_XPSPcolors derived from corrected Gaia BP/RP (XP) spectra to obtain precise estimates of metallicity for about 100 million stars in the Milky Way (34 million giants in the color range 0.6 < (BP–RP)₀ < 1.75 and 65 million dwarfs in the color range 0.2 < (BP–RP)₀ < 1.5). The submillimagnitude precision of the derived synthetic stellar colors enables estimates of metallicity for stars as low as [Fe/H] ∼ −4. Multiple validation tests indicate that the typical metallicity precision is between 0.05 and 0.1 dex for both dwarfs and giants at [Fe/H] = 0, as faint asG ∼ 16, and decreases to 0.15–0.25 dex at [Fe/H] = −2.0. For −4.0 < [Fe/H] < −3.0, the typical metallicity precision decreases to on the order of 0.4–0.5 dex, based on the results from the reference sample. Our achieved precision is comparable to or better than previous efforts using the entire XP spectra and about 3 times better than our previous work based on Gaia EDR3 colors. This opens up new opportunities for investigations of stellar populations, the formation and chemical evolution of the Milky Way, the chemistry of stars and star clusters, and the identification of candidate stars for subsequent high-resolution spectroscopic follow-up. The catalog is publicly available at doi:10.12149/101548. 

Abstract We design an uncertainty-aware cost-sensitive neural network (UA-CSNet) to estimate metallicities from dereddened and corrected Gaia BP/RP (XP) spectra for giant stars. This method accounts for both stochastic errors in the input spectra and the imbalanced density distribution in [Fe/H] values. With a specialized architecture and training strategy, the UA-CSNet improves the precision of the predicted metallicities, especially for very metal-poor (VMP; [Fe/H] ≤ −2.0) stars. With the PASTEL catalog as the training sample, our model can estimate metallicities down to [Fe/H] ∼ −4. We compare our estimates with a number of external catalogs and conduct tests using star clusters, finding overall good agreement. We also confirm that our estimates for VMP stars are unaffected by carbon enhancement. Applying the UA-CSNet, we obtain reliable and precise metallicity estimates for approximately 20 million giant stars, including 360,000 VMP stars and 50,000 extremely metal-poor ([Fe/H] ≤ −3.0) stars. The resulting catalog is publicly available via the Chinese Virtual Observatory at doi: 10.12149/101604. This work highlights the potential of low-resolution spectra for metallicity estimation and provides a valuable data set for studying the formation and chemodynamical evolution of our Galaxy.