Based on the Sloan Digital Sky Survey and South Galactic Cap u-band Sky Survey (SCUSS), we simulate the photometric metallicity distribution functions (MDFs) of stars in the Galactic halo. The photometric metallicity of stars was estimated by a new Monte-Carlo method. Due to the use of a more reliable metallicity calibration method and more accurate u-band deep measurements from SCUSS, we can obtain more accurate MDFs of a large sample of distant stars in the Galactic halo. In this study, we select 78,092 F/G main-sequence turnoff stars (MSTO) in the south Galactic cap, with 0.2 < (g − r)0 < 0.4, as tracers of the stellar MDFs in the Galactic halo. The sample stars are divided into two height intervals above the Galactic plane: −8 < z < −4 kpc and −12 < z < −8 kpc. The MDFs of selected stars in each interval are well fit by a three-Gaussian model, with peaks at [Fe/H] ≈ −0.63, −1.45, and −2.0. The two metal-poor components correspond to the inner halo and outer halo, respectively. The fraction of the metal-rich component, which may be contributed by the substructure (such as Sagittarius stream or other streams) is about 10%. With limited kinematic estimation, we find the correlations between metallicity and kinematics. Our results provide additional supporting evidence of duality of the Galactic halo.

Since Gaia DR2 was released, many velocity structures in the disk have been revealed such as large scale ridge-like patterns in the phase space. Both kinematic information and stellar elemental abundances are needed to reveal their evolution history. We have used labels from the APOGEE survey to predict elemental abundances for a huge amount of low resolution spectra from the LAMOST survey. Deep learning with artificial neural networks can automatically draw on physically sensible features in the spectrum for their predictions. Abundances of 12 individual elements: [C/Fe], [N/Fe], [O/Fe], [Mg/Fe], [Al/Fe], [Si/Fe], [S/Fe], [Cl/Fe], [Ca/Fe], [Ti/Fe], [Mn/Fe] and [Ni/Fe] along with basic stellar labels T eff , log g, metallicity ([M/H] and [Fe/H]) and [α/M] for 1 063 386 stars have been estimated.Then those stars were cross matched with Gaia DR2 data to obtain kinematic parameters. We presented distributions of chemical abundances in the V φ versus R coordinate. Our results extend the chemical characterization of the ridges in the (R, V φ ) plane to about R =13 kpc toward the anticenter direction. In addition, radial elemental abundance gradients for disk stars with abs(z )< 0.5 kpc are investigated and we fitted a line for median abundance values of bins of stars with galactocentric distance between R > 7.84 kpc and R < 15.84 kpc. The radial metallicity gradients for disk stars are respectively -0.0475 ±0.0015 for R < 13.09 kpc and -0.0173 ±0.0028 for R < 13.09 kpc. Gradients for other elemental abundances are also obtained, for example, [α/M] gradients for disk stars is 0.0030 ±0.0002.

Extracting accurate atmospheric parameters and elemental abundances from stellar spectra is crucial for studying the Galactic evolution. In this paper, a deep neural network architecture named StarNet is used to estimate stellar parameters (T eff , log g, [M/H]), α-elements as well as C and N abundances from LAMOST spectra, using stars in common with APOGEE survey as training data set. With the spectral signal-to-noise ratio (S/N) in g band (S/N g ) larger than 10, the test indicates our method yields uncertainties of 45 K for T eff , 0.1 dex for log g, 0.05 dex for [M/H], 0.03 dex for [α/M], 0.06 dex for [C/M] and 0.07 dex for [N/M]. Because of few stars with [M/H] < −1.0 dex in the training set, the uncertainties are dominated by stars with [M/H] > −1.0 dex. Based on test results, we think StarNet is valid for measuring parameters from low-resolution spectra of the LAMOST survey. The trained network is then used to predict parameters for 938,720 giants from LAMOST DR5. Within the range of stellar parameters 4000 K < T eff <5300 K, 0 dex < log g < 3.8 dex and −2.5 dex < [M/H] < 0.5 dex, the comparisons with high-precision measurements (e.g., PASTEL, asteroseismic log g) yield uncertainties of 100 K for T eff , 0.10 dex for log g, 0.12 dex for [M/H]. Our estimations are consistent with values from the high-precision measurements. In this research, a deep neural network is successfully applied on the numerous spectra from LAMOST. The deep neural network shows an excellent performance, which demonstrates that deep learning can effectively reduce the inconsistencies between parameters measured by the individual survey pipelines.

Based on Sloan Digital Sky Survey (SDSS) photometric data, Gu developed a new Monte-Carlo-based method for estimating the stellar metallicity distribution functions (MDFs). This method enables a more reliable determination of MDFs compared with the conventional polynomial-based methods. In this work, MDF determined from the method are well fit by three-Gaussian model, with peaks at [Fe/H]=−0.68, −1.38, and −1.90, associated with the thick disk, inner halo, and outer halo, respectively. The vertical metallicity gradient within 1 < Z < 5 kpc is d [Fe/H] /dZ ≈ −0.19 dex · kpc −1 around R = 8.25 kpc. But the mean radial gradient is almost negligible. The density profile of the thick disk is fitted with modified double exponential law decaying to a constant at far distance. The scale height and scale length thus estimated are H ≈ 1.13 kpc and L ≈ 3.63 kpc, which are in consistent with the results determined from star-counts method in previous studies. The halos are described with two-axial power-law ellipsoid and the axis ratios of both inner halo and outer halo, inferred from stellar number density in R-Z plane, are q ih ≈ 0.49 and q oh ≈ 0.61, respectively. It also manifests that the outer halo is a more spherical than inner halo. Moreover, the halo power-law indices estimated are n ih ≈ 3.4 and n oh ≈ 3.1, indicating that the stellar number density of inner halo changes more steeper than that of outer halo.