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With new catalogues arriving such as the Gaia DR1, containing more than a billion objects, new methods of handling and visualizing these data volumes are needed. We show that by calculating statistics on a regular (N-dimensional) grid, visualizations of a billion objects can be done within a second on a modern desktop computer. This is achieved using memory mapping of hdf5 files together with a simple binning algorithm, which are part of a Python library called vaex. This enables efficient exploration or large datasets interactively, making science exploration of large catalogues feasible. Vaex is a Python library and an application, which allows for interactive exploration and visualization. The motivation for developing vaex is the catalogue of the Gaia satellite, however, vaex can also be used on SPH or N-body simulations, any other (future) catalogues such as SDSS, Pan-STARRS, LSST, etc. or other tabular data. The homepage for vaex is http://vaex.astro.rug.nl. [ABSTRACT FROM PUBLISHER]

We present a proper-motion study on models of the dwarf spheroidal galaxy Sculptor, based on the predicted proper-motion accuracy of Gaia measurements. Gaia will measure proper motions of several hundreds of stars for a Sculptor-like system. Even with an uncertainty on the proper motion of order 1.5 times the size of an individual proper-motion value of ~10 mas/century, we find that it is possible to recover Sculptor's systemic proper motion at its distance of 79 kpc. [ABSTRACT FROM AUTHOR]

The detailed study of the Galactic stellar halo may hold the key to unlocking the assembly history of the Milky Way. Here, we present a machine learning model for selecting metal poor stars from the TGAS catalogue using 5 dimensional phase-space information, coupled with optical and near-IR photometry. We characterise the degree of substructure in our halo sample in the Solar neighbourhood by measuring the velocity correlation function. [ABSTRACT FROM PUBLISHER]