The orbits of the least chemically enriched stars open a window on the formation of our Galaxy when it was still in its infancy. The common picture is that these lowmetallicity stars are distributed as an isotropic, pressure-supported component since these stars were either accreted from the early building blocks of the assembling Milky Way, or were later brought by the accretion of faint dwarf galaxies. Combining the metallicities and radial velocities from the Pristine and LAMOST surveys and Gaia DR2 parallaxes and proper motions for an unprecedented large and unbiased sample of very metal-poor stars at [Fe/H] ≤ −2.5 we show that this picture is incomplete. This sample shows strong statistical evidence (at the 5.0σ level) of asymmetry in their kinematics, favouring prograde motion. Moreover, we find that 31% of the stars that currently reside in the disk do not venture outside of the disk plane throughout their orbit. The discovery of this population implies that a significant fraction of stars with iron abundances [Fe/H] ≤ −2.5 formed within or concurrently with the Milky Way disk and that the history of the disk was quiet enough to allow them to retain their disk-like orbital properties.

Spectroscopic surveys require fast and efficient analysis methods to maximize their scientific impact. Here we apply a deep neural network architecture to analyze both SDSS-III APOGEE DR13 and synthetic stellar spectra. When our convolutional neural network model (StarNet) is trained on APOGEE spectra, we show that the stellar parameters (temperature, gravity, and metallicity) are determined with similar precision and accuracy as the APOGEE pipeline. StarNet can also predict stellar parameters when trained on synthetic data, with excellent precision and accuracy for both APOGEE data and synthetic data, over a wide range of signal-to-noise ratios. In addition, the statistical uncertainties in the stellar parameter determinations are comparable to the differences between the APOGEE pipeline results and those determined independently from optical spectra. We compare StarNet to other data-driven methods; for example, StarNet and the Cannon 2 show similar behaviour when trained with the same datasets, however StarNet performs poorly on small training sets like those used by the original Cannon. The influence of the spectral features on the stellar parameters is examined via partial derivatives of the StarNet model results with respect to the input spectra. While StarNet was developed using the APOGEE observed spectra and corresponding ASSET synthetic data, we suggest that this technique is applicable to other wavelength ranges and other spectral surveys.

High-resolution optical spectra of 30 metal-poor stars selected from the Pristine survey are presented, based on observations taken with the Gemini Observatory GRACES spectrograph. Stellar parameters Teff and log g are determined using a Gaia DR2 colour-temperature calibration and surface gravity from the Stefan-Boltzmann equation. GRACES spectra are used to determine chemical abundances (or upper-limits) for 20 elements (Li, O, Na, Mg, K, Ca, Ti, Sc, Cr, Mn, Fe, Ni, Cu, Zn, Y, Zr, Ba, La, Nd, Eu). These stars are confirmed to be metal-poor ([Fe/H] < −2.5), with higher precision than from earlier medium-resolution analyses. The chemistry for most targets is similar to other extremely metal-poor stars in the Galactic halo. Three stars near [Fe/H] = −3.0 have unusually low Ca and high Mg, suggestive of contributions from few SN II where alpha-element formation through hydrostatic nucleosynthesis was more efficient. Three new carbon-enhanced metal-poor stars are also identified (two CEMP-s and one potential CEMP-no star) when our chemical abundances are combined with carbon from previous medium-resolution analyses. The GRACES spectra also provide precision radial velocities (σRV ≤ 0.2km s−1) for dynamical orbit calculations with the Gaia DR2 proper motions. Most of our targets are dynamically associated with the Galactic halo; however, five stars with [Fe/H] < −3 have planar-like orbits, including one retrograde star. Another five stars are dynamically consistent with the Gaia-Sequoia accretion event; three have typical halo [α/Fe] ratios for their metallicities, whereas two are [Mg/Fe]-deficient, and one is a new CEMP-s candidate. These results are discussed in terms of the formation and early chemical evolution of the Galaxy.

In the current era of stellar spectroscopic surveys, synthetic spectral libraries are the basis for the derivation of stellar parameters and chemical abundances. In this paper, we compare the stellar parameters determined using five popular synthetic spectral grids (INTRIGOSS, FERRE, AMBRE, PHOENIX, and MPIA/1DNLTE) with our convolutional neural network (CNN, StarNet). The stellar parameters are determined for six physical properties (effective temperature, surface gravity, metallicity, [α/Fe], radial velocity, and rotational velocity) given the spectral resolution, signal-to-noise, and wavelength range of optical FLAMES-UVES spectra from the Gaia-ESO Survey. Both CNN modelling and epistemic uncertainties are incorporated through training an ensemble of networks. StarNet training was also adapted to mitigate differences between the synthetic grids and observed spectra by augmenting with realistic observational signatures (i.e. resolution matching, wavelength sampling, Gaussian noise, zeroing flux values, rotational and radial velocities, continuum removal, and masking telluric regions). Using the FLAMES-UVES spectra for FGK type dwarfs and giants as a test set, we quantify the accuracy and precision of the stellar label predictions from StarNet. We find excellent results over a wide range of parameters when StarNet is trained on the MPIA/1DNLTE synthetic grid, and acceptable results over smaller parameter ranges when trained on the 1DLTE grids. These tests also show that our CNN pipeline is highly adaptable to multiple simulation grids.