SIMBAD references

Context. Several large stellar spectroscopic surveys are producing enormous amounts of data that can be used to determine stellar atmospheric parameters and chemical abundances. Nonetheless, the accuracy achieved in the derived astrophysical parameters is still insufficient, mainly because of the paucity of adequate calibrators, particularly in the metal-poor regime ([Fe/H]≤-1.0).
Aims. Our aim is to increase the number of metal-poor stellar calibrators that have accurate parameters. Here, we introduce the TITANS metal-poor reference stars: a sample of 41 dwarf and subgiant stars with accurate, but model-dependent, parameters.
Methods. Effective temperatures (T_eff) were derived by fitting observed Hα profiles with synthetic lines computed using three-dimensional (3D) hydrodynamic model atmospheres that take local thermodynamic equilibrium departures into account (non-LTE effects). Surface gravities (log g) were computed using evolutionary tracks and parallaxes from Gaia early-data release 3.
Results. The same methods recover the T_eff values of the Gaia benchmark stars, which are mostly based on interferometric measurements, with a 1σ dispersion of ±50K. We assume this to be the accuracy of the Hα profiles computed from 3D non-LTE models for metal-poor dwarfs and subgiants, although this is likely an upper-bound estimate dominated by the uncertainty of the standard T_eff values. We achieved an internal precision typically between 30 and 40K; these errors are dominated by instrumental effects. The final total uncertainty for the T_eff values of the TITANS are thus estimated to be of the order of 1%. The typical error for log g is ≤0.04dex. In addition, we identified a few members of Gaia-Enceladus, of Sequoia, and of the Helmi stream in our sample. These stars can pave the way for the accurate chemical characterisation of these Galactic substructures.
Conclusions. Using the TITANS as a reference, large stellar surveys will be able to improve the internal calibration of their astrophysical parameters. Ultimately, this sample will help users of data from Gaia and large surveys to reach their goal of redefining our understanding of stars, stellar systems, and the Milky Way.