Collaborative Visual Analytics of Large Radio Surveys

Radio survey datasets comprise an increasing number of individual observations stored as sets of multi-dimensional data. In large survey projects, astronomers commonly face limitations regarding: 1) interactive visual analytics of sufficiently large subsets of data; 2) synchronous and asynchronous collaboration; and 3) documentation of the discovery workflow. To support collaborative data inquiry, we present encube, a large scale comparative visual analytics framework. encube can utilise large tiled-displays such as the CAVE2 (a hybrid 2D and 3D virtual reality environment powered with a 100 Tflop/s GPU-based supercomputer) for collaborative analysis of large subsets of data from radio surveys. It also works on standard desktops, providing a seamless visual analytics experience regardless of the display ecology. At the heart of encube is a data management unit built in Python - making it simple to incorporate other Python-based astronomical packages and Virtual Observatory capabilities developed within our community. We discuss how encube builds a bridge between the CAVE2 and the classical desktop, preserving all traces of the work completed on either platform - providing a research process that can be continuous regardless of location.