The observational parameter space that allows us to detect and describe nonsingle stars is enormous. It comes from the fact that binary stars are very numerous, present themselves with a huge variety of physical properties and have signatures in all astronomical fundamental techniques (astrometry, photometry, spectroscopy). It is, therefore, not a surprise that any significant improvement in observational astronomical facilities has an important impact on our knowledge of binaries. We are currently in an era where the development of various large-scale surveys is impressive. Among them, Gaia and LSST are exceptional surveys that have and likely will have a profound and long-lasting impact on the astronomical landscape. This chapter reviews the status of these two projects, and considers how they improve our knowledge of binary stars.

Many aspects of the evolution of stars, and in particular the evolution of binary stars, are beyond our ability to model them in detail. Instead, we rely on observations to guide our often phenomenological models and pin down uncertain model parameters. To do this statistically requires population synthesis. Populations of stars modelled on computers are compared to populations of stars observed with our best telescopes. The closest match between observations and models provides insight into unknown model parameters and hence the underlying astrophysics. This chapter reviews the impact that modern big-data surveys will have on population synthesis, the large parameter space problem that is rife for the application of modern data science algorithms and some examples of how population synthesis is relevant to modern astrophysics.