page title icon Exploring and interrogating astrophysical data in virtual reality

Scientists across all disciplines increasingly rely on machine learning algorithms to analyse and sort datasets of ever increasing volume and complexity. These algorithms are great at extracting trends and outliers, and automate the data analysis process to a great extent, but science being also about not missing the unexpected, scientists still need to be able to closely inspect their data and detect possible errors by the artificial intelligence analysing the data, or instrument -related systematics. But the scale of the data being so large, such inspection is only possible if we have suitable technology to do so. Thanks to the world of computer games, virtual reality (VR) headsets and special computer processors called GPUs, this is now possible. In this paper, the IDIA Visualisation Lab team describe how they have deployed such technology to interact with large, and complex data. The team has developed custom-built interactive VR tools, called the iDaVIE suite, that are are useful for several areas of research in astronomy, including galaxy evolution, cosmic large-scale structure, galaxy–galaxy interactions, and gas/kinematics of nearby galaxies in survey and targeted observations.

In the new era of Big Data ushered in by major facilities such as the SKA and LSST that render past analysis and refinement methods highly constrained, a paradigm shift to new software, technology and methods that exploit the power of visual perception, will play an increasingly important role in bridging the gap between statistical metrics and new discovery.

A beta version of the iDaVIE software system that is free and open to the community was released by the Vis Lab team along with an open access journal article published in specialist journal Astronomy and Computing.

Reference:
Exploring and interrogating astrophysical data in virtual reality, T.H. Jarrett, A. Comrie, L. Marchetti, A. Sivitilli, S. Macfarlane, F. Vitello, U. Becciani, A.R. Taylor, J.M. van der Hulst, P. Serra, N. Katz, M.E. Cluver, Astronomy and Computing 37 (2021) 100502