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Context. Rapidly growing datasets from stellar spectroscopic surveys are providing unprecedented opportunities to analyse the chemical evolution history of our Galaxy. However, spectral analysis requires accurate modelling of synthetic stellar spectra for late-type stars, for which the assumption of local thermodynamic equilibrium (LTE) has been shown to be insufficient in many cases. Errors associated with LTE can be particularly large for Ti I, which is susceptible to over-ionisation, particularly in metal-poor stars. Aims. The aims of this work are to study and quantify the 1D non-LTE effects on titanium abundances across the Hertzsprung-Russell diagram for a large sample of stars. Methods. A large grid of departure coefficients, βν, were computed on standard MARCS model atmospheres. The grid extends from 3000 K to 8000 K in Teff, −0.5 dex to +5.5 dex in log g, and −5.0 to +1.0 in [Fe/H], with non-LTE effects in this grid reaching up to 0.4 dex. This was used to compute abundance corrections that were subsequently applied to the LTE abundances of over 70 000 stars selected from the GALAH survey in addition to a smaller sample of literature Keck data for metal-poor dwarfs. Results. The non-LTE effects grow towards lower [Fe/H], lower log g, and higher Teff, with a minimum and maximum ΔA(Ti)Ti I of 0.02 and 0.19 in the GALAH sample. For metal-poor giants, the non-LTE modelling reduces the average ionisation imbalance (ΔI−II) from −0.11 dex to −0.01 dex at [Fe/H] = −1.7, and the enhancement in titanium abundances from Ti I lines results in a [Ti/Fe] versus [Fe/H] trend that more closely resembles the behaviour of Ti II at low metallicities. At higher metallicities, the results are limited by the precision of the GALAH DR3 LTE abundances and the effects are within the errors. For the most metal-poor dwarfs from the Keck sample, the average ionisation imbalance increases from −0.1 dex to +0.2 dex, a shortcoming that is consistent with previous 1D non-LTE studies and which we speculate could be related to 3D effects. Conclusions. Non-LTE effects on titanium abundances are significant. Neglecting them may alter our understanding of Galactic chemical evolution. We have made our grid of departure coefficients publicly available, with the caveat that the Ti abundances of metal-poor dwarfs need further study in 3D non-LTE. [ABSTRACT FROM AUTHOR]

Context. In June 2022, Gaia DR3 provided the astronomy community with about one million spectra from the Radial Velocity Spectrometer (RVS) covering the CaII triplet region. In the next Gaia data releases, we anticipate the number of RVS spectra to successively increase from several 10 million spectra to eventually more than 200 million spectra. Thus, stellar spectra are projected to be produced on an 'industrial scale', with numbers well above those for current and anticipated ground-based surveys. However, one-third of the published spectra have 15 ≤ S /N ≤ 25 per pixel such that they pose problems for classical spectral analysis pipelines, and therefore, alternative ways to tap into these large datasets need to be devised. Aims. We aim to leverage the versatility and capabilities of machine learning techniques for supercharged stellar parametrisation by combining Gaia-RVS spectra with the full set of Gaia products and high-resolution, high-quality ground-based spectroscopic reference datasets. Methods. We developed a hybrid convolutional neural network (CNN) that combines the Gaia DR3 RVS spectra, photometry (G, G_BP, G_RP), parallaxes, and XP coefficients to derive atmospheric parameters (Teff, log(g) as well as overall [M/H]) and chemical abundances ([Fe/H] and [α/M]). We trained the CNN with a high-quality training sample based on APOGEE DR17 labels. Results. With this CNN, we derived homogeneous atmospheric parameters and abundances for 886 080 RVS stars that show remarkable precision and accuracy compared to external datasets (such as GALAH and asteroseismology). The CNN is robust against noise in the RVS data, and we derive very precise labels down to S/N =15. We managed to characterise the [α/M] - [M/H] bimodality from the inner regions to the outer parts of the Milky Way, which has never been done using RVS spectra or similar datasets. Conclusions. This work is the first to combine machine learning with such diverse datasets and paves the way for large-scale machine learning analysis of Gaia-RVS spectra from future data releases. Large, high-quality datasets can be optimally combined thanks to the CNN, thereby realising the full power of spectroscopy, astrometry, and photometry. [ABSTRACT FROM AUTHOR]

The Pristine survey uses narrow-band photometry to derive precise metallicities down to the extremely metal-poor regime (⁠|$ \rm [Fe/H] \lt -3$|⁠), and currently consists of over 4 million FGK-type stars over a sky area of |$\sim 2500\, \mathrm{deg}^2$|⁠. We focus our analysis on a subsample of ∼80 000 main-sequence turn-off stars with heliocentric distances between 6 and 20 kpc, which we take to be a representative sample of the inner halo. The resulting metallicity distribution function (MDF) has a peak at |$ \rm [Fe/H] =-1.6$|⁠ , and a slope of Δ(LogN)/ |$\Delta \rm [Fe/H] = 1.0 \pm 0.1$| in the metallicity range of |$-3.4\; \lt\; \rm [Fe/H]\; \lt -2.5$|⁠. This agrees well with a simple closed-box chemical enrichment model in this range, but is shallower than previous spectroscopic MDFs presented in the literature, suggesting that there may be a larger proportion of metal-poor stars in the inner halo than previously reported. We identify the Monoceros/TriAnd/ACS/EBS/A13 structure in metallicity space in a low-latitude field in the anticentre direction, and also discuss the possibility that the inner halo is dominated by a single, large merger event, but cannot strongly support or refute this idea with the current data. Finally, based on the MDF of field stars, we estimate the number of expected metal-poor globular clusters in the Milky Way halo to be 5.4 for |$ \rm [Fe/H]\; \lt\; -2.5$| and 1.5 for |$ \rm [Fe/H]\; \lt\; -3$|⁠ , suggesting that the lack of low-metallicity globular clusters in the Milky Way is not due simply to statistical undersampling. [ABSTRACT FROM AUTHOR]

Our Galaxy is known to contain a central boxy/peanut-shaped bulge, yet the importance of a classical, pressure-supported component within the central part of the Milky Way is still being debated. It should be most visible at low metallicity, a regime that has not yet been studied in detail. Using metallicity-sensitive narrow-band photometry, the Pristine Inner Galaxy Survey (PIGS) has collected a large sample of metal-poor (⁠|$\rm {[Fe/H]}\, \lt -1.0$|⁠) stars in the inner Galaxy to address this open question. We use PIGS to trace the metal-poor inner Galaxy kinematics as function of metallicity for the first time. We find that the rotational signal decreases with decreasing [Fe/H] , until it becomes negligible for the most metal-poor stars. Additionally, the velocity dispersion increases with decreasing metallicity for |$-3.0 \lt \rm {[Fe/H]}\, \lt -0.5$|⁠ , with a gradient of −44 ± 4 km s−1 dex−1. These observations may signal a transition between Galactic components of different metallicities and kinematics, a different mapping on to the boxy/peanut-shaped bulge for former disc stars of different metallicities and/or the secular dynamical and gravitational influence of the bar on the pressure-supported component. Our results provide strong constraints on models that attempt to explain the properties of the inner Galaxy. [ABSTRACT FROM AUTHOR]

We use Gaia DR2 astrometric and line-of-sight velocity information combined with two sets of distances obtained with a Bayesian inference method to study the 3D velocity distribution in the Milky Way disc. We search for variations in all Galactocentric cylindrical velocity components (V ϕ, VR , and Vz) with Galactic radius, azimuth, and distance from the disc mid-plane. We confirm recent work showing that bulk vertical motions in the R – z plane are consistent with a combination of breathing and bending modes. In the x – y plane, we show that, although the amplitudes change, the structure produced by these modes is mostly invariant as a function of distance from the plane. Comparing to two different Galactic disc models, we demonstrate that the observed patterns can drastically change in short time intervals, showing the complexity of understanding the origin of vertical perturbations. A strong radial VR gradient was identified in the inner disc, transitioning smoothly from 16 km s−1 kpc−1 at an azimuth of 30° < ϕ < 45° ahead of the Sun-Galactic centre line to −16 km s−1 kpc−1 at an azimuth of −45° < ϕ < −30° lagging the solar azimuth. We use a simulation with no significant recent mergers to show that exactly the opposite trend is expected from a barred potential, but overestimated distances can flip this trend to match the data. Alternatively, using an N -body simulation of the Sagittarius dwarf–Milky Way interaction, we demonstrate that a major recent perturbation is necessary to reproduce the observations. Such an impact may have strongly perturbed the existing bar or even triggered its formation in the last 1–2 Gyr. [ABSTRACT FROM AUTHOR]

The Pristine survey is a narrow-band, photometric survey focused around the wavelength region of the Ca II H&K absorption lines, designed to efficiently search for extremely metalpoor stars. In this work, we use the first results of amedium-resolution spectroscopic follow-up to refine the selection criteria for finding extremely metal-poor stars ([Fe/H] ≤ -3.0) in the Pristine survey. We consider methods by which stars can be selected from available broadband and infrared photometry plus the additional Pristine narrow-band photometry. The sample consists of 205 stars in the magnitude range 14 < V < 18. Applying the photometric selection criteria cuts the sample down to 149 stars, and from these we report a success rate of 70 per cent for finding stars with [Fe/H] ≤ -2.5 and 22 per cent for finding stars with [Fe/H] ≤ -3.0. These statistics compare favourably with other surveys that search for extremely metal-poor stars, namely an improvement by a factor of ~4 - 5 for recovering stars with [Fe/H]≤-3.0. In addition, Pristine covers a fainter magnitude range than its predecessors and can thus probe deeper into the Galactic halo. [ABSTRACT FROM AUTHOR]

The paucity of hypervelocity stars (HVSs) known to date has severely hampered their potential to investigate the stellar population of the Galactic Centre and the Galactic potential. The first Gaia data release (DR1, 2016 September 14) gives an opportunity to increase the current sample. The challenge is the disparity between the expected number of HVSs and that of bound background stars. We have applied a novel data mining algorithm based on machine learning techniques, an artificial neural network, to the Tycho-Gaia astrometric solution catalogue. With no pre-selection of data, we could exclude immediately ~99 per cent of the stars in the catalogue and find 80 candidates with more than 90 per cent predicted probability to be HVSs, based only on their position, proper motions and parallax. We have crosschecked our findings with other spectroscopic surveys, determining radial velocities for 30 and spectroscopic distances for five candidates. In addition, follow-up observations have been carried out at the Isaac Newton Telescope for 22 stars, for which we obtained radial velocities and distance estimates. We discover 14 stars with a total velocity in the Galactic rest frame >400 km s-1, and five of these have a probability of >50 per cent of being unbound from the Milky Way. Tracing back their orbits in different Galactic potential models, we find one possible unbound HVS with v ~ 520 km s-1, five bound HVSs and, notably, five runaway stars with median velocity between 400 and 780 km s-1. At the moment, uncertainties in the distance estimates and ages are too large to confirm the nature of our candidates by narrowing down their ejection location, and we wait for future Gaia releases to validate the quality of our sample. This test successfully demonstrates the feasibility of our new data-mining routine. [ABSTRACT FROM AUTHOR]

Extremely metal-poor (EMP) stars are old objects formed in the first Gyr of the Universe. They are rare and, to select them the most successful strategy has been to build on large and low-resolution spectroscopic surveys. The combination of narrow- and broad-band photometry provides a powerful and cheaper alternative to select metal-poor stars. The ongoing Pristine Survey is adopting this strategy, conducting photometry with the Canada France Hawaii Telescope MegaCam wide-field imager and a narrow-band filter centered at 395.2 nm on the Ca II-H and -K lines. In this paper, we present the results of the spectroscopic follow-up conducted on a sample of 26 stars at the bright end of the magnitude range of the Survey ( g⩽15), using FEROS at the MPG/ESO 2.2-m telescope (manufactured by Zeiss, Oberkochen, Germany). From our chemical investigation on the sample, we conclude that this magnitude range is too bright to use the Sloan Digital Sky Survey (SDSS) g r i bands, which are typically saturated. Instead, the Pristine photometry can be usefully combined with the AAVSO Photometric All Sky Survey (APASS) g r iphotometry to provide reliable metallicity estimates. [ABSTRACT FROM AUTHOR]

Hypervelocity stars (HVSs) are characterized by a total velocity in excess of the Galactic escape speed, and with trajectories consistent with coming from the Galactic Centre. We apply a novel data mining routine, an artificial neural network, to discover HVSs in the TGAS subset of the first data release of the Gaia satellite, using only the astrometry of the stars. We find 80 stars with a predicted probability >90% of being HVSs, and we retrieved radial velocities for 47 of those. We discover 14 objects with a total velocity in the Galactic rest frame >400 km s−1, and 5 of these have a probability >50% of being unbound from the Milky Way. Tracing back orbits in different Galactic potentials, we discover 1 HVS candidate, 5 bound HVS candidates, and 5 runaway star candidates with remarkably high velocities, between 400 and 780 km s−1. We wait for future Gaia releases to confirm the goodness of our sample and to increase the number of HVS candidates. [ABSTRACT FROM PUBLISHER]

The chemical abundances for five metal-poor stars in and towards the Galactic bulge have been determined from the H-band infrared spectroscopy taken with the RAVEN multi-object adaptive optics science demonstrator and the Infrared Camera and Spectrograph at the Subaru 8.2-m telescope. Three of these stars are in the Galactic bulge and have metallicities between -2.1<[Fe/H]<-1.5, and high [α/Fe]~+0.3, typical of Galactic disc and bulge stars in this metallicity range; [Al/Fe] and [N/Fe] are also high, whereas [C/Fe] < +0.3. An examination of their orbits suggests that two of these stars may be confined to the Galactic bulge and one is a halo trespasser, though proper motion values used to calculate orbits are quite uncertain. An additional two stars in the globular cluster M22 show [Fe/H] values consistent to within 1σ, although one of these two stars has [Fe/H] = -2.01 ± 0.09, which is on the low end for this cluster. The [α/Fe] and [Ni/Fe] values differ by 2σ, with the most metal-poor star showing significantly higher values for these elements. M22 is known to show element abundance variations, consistent with a multipopulation scenario though our results cannot discriminate this clearly given our abundance uncertainties. This is the first science demonstration of multiobject adaptive optics with high-resolution infrared spectroscopy, and we also discuss the feasibility of this technique for use in the upcoming era of 30-m class telescope facilities. [ABSTRACT FROM AUTHOR]