Survey-scale discovery-based research processes: Evaluating a bespoke visualisation environment for astronomical survey data

TL;DR

This paper evaluates a custom visualisation environment for astronomical survey data, demonstrating that parallel, comparative visualisation significantly accelerates human analysis tasks on large spectral cube datasets.

Contribution

It introduces and benchmarks a bespoke visual analytics framework, encube, for efficient parallel visualisation of large-scale survey data on a high-resolution display wall.

Findings

Parallel visualisation reduces analysis time by over 100 times.

Loading 180 spectral cubes takes under 5 minutes, scaling linearly with data size.

The environment enables interactive comparison of large 3D datasets, improving workflow efficiency.

Abstract

Next generation astronomical surveys naturally pose challenges for human-centred visualisation and analysis workflows that currently rely on the use of standard desktop display environments. While a significant fraction of the data preparation and analysis will be taken care of by automated pipelines, crucial steps of knowledge discovery can still only be achieved through various level of human interpretation. As the number of sources in a survey grows, there is need to both modify and simplify repetitive visualisation processes that need to be completed for each source. As tasks such as per-source quality control, candidate rejection, and morphological classification all share a single instruction, multiple data (SIMD) work pattern, they are amenable to a parallel solution. Selecting extragalactic neutral hydrogen (HI) surveys as a representative example, we use system performance benchmarking and the visual data and reasoning (VDAR) methodology from the field of information visualisation to evaluate a bespoke comparative visualisation environment: the encube visual analytics framework deployed on the 83 Megapixel Swinburne Discovery Wall. Through benchmarking using spectral cube data from existing HI surveys, we are able to perform interactive comparative visualisation via texture-based volume rendering of 180 three-dimensional (3D) data cubes at a time. The time to load a configuration of spectral cubes scale linearly with the number of voxels, with independent samples of 180 cubes (8.4 Gigavoxels or 34 Gigabytes) each loading in under 5 minutes. We show that parallel comparative inspection is a productive and time-saving technique which can reduce the time taken to complete SIMD-style visual tasks currently performed at the desktop by at least two orders of magnitude, potentially rendering some labour-intensive desktop-based workflows obsolete.