VRDAV is: remote visualisation of astronomy data with a standalone virtual reality device - Van Zyl, Michaela (Department of Computer Science, 2026)
This dissertation explores the challenges of visualising vast astronomy data cubes using a virtual reality environment, addressing the ever-increasing volume of data collected by radio astronomy instruments. As the amount of data grows year after year—ranging from terabytes to petabytes—radio astronomy researchers face significant difficulties in processing, storing, and analysing this immense data. The visual analysis of the collected data is a crucial part of radio astronomy research. Traditional visualisation tools are often inadequate due to the size and complexity of the data. The Data far exceeds the computational capabilities of devices like laptops or home desktop computers. Researchers are often required to either access specialised systems or analyse small portions of the data at a time. Specialised systems are typically locked to specific locations and inaccessible to many, and segmenting the data can obscure the broader context of a dataset. These points highlight the need for a new approach to overcome the presented limitations. The objective of this research is to develop a prototype system that enables the visualization of large astronomy data cubes in a virtual reality environment using a standalone VR headset. The system is specifically designed to operate on devices with limited computational power, such as laptops and VR headsets. Making it accessible to a wider range of users. The research addresses key questions, including the feasibility of remote implementation, the scalability of the system for handling large datasets, and a performance comparison with existing astronomy visualisation systems. The VRDAVis system design follows a client-server architecture, where the client-side communicates with the server to visualise large astronomy data cubes. These client devices are either a computer or standalone VR headset. The system pre-processes the data into multiple resolution levels, these levels are referred to as mipmaps, to reduce the computational load on the client. The front-end, built as a web-based application, allows users to select data cubes and progressively visualise different levels of detail. The resolution levels start from a low-resolution overview to higher resolution as a user zooms in on areas of interest. The client and server communicate via WebSock-ets, and WebRTC is used for peer-to-peer connections when transferring the application's state between devices (e.g., from desktop to VR). The VRDAVis system was tested through an qualitative study, with participants who were astronomy researchers familiar with VR technology. The participants were asked to perform various actions using VRDAVis. The tasks involved selecting a file on the laptop, transferring the session to the VR head-set, and interacting with the data cube. Key findings were gathered from user feedback, focusing on their experience with the system's usability, interaction with the visualizations, and the overall workflow. Observations on task performance and any difficulties encountered were also collected, along with participants' impressions of working in the VR environment.