Collaborative Workspaces to Accelerate Discovery

Abstract

By applying a display ecology to the Deeper, Wider, Faster proactive, simultaneous telescope observing campaign, we have shown a dramatic reduction in the time taken to inspect DECam CCD images for potential transient candidates and to produce time-critical triggers to standby telescopes. We also show how facilitating rapid corroboration of potential candidates and the exclusion of non-candidates improves the accuracy of detection; and establish that a practical and enjoyable workspace can improve the experience of an otherwise taxing task for astronomers. We provide a critical road-test of two advanced displays in a research context -a rare opportunity to demonstrate how they can be used rather than simply discuss how they might be used to accelerate discovery.

Immersive Analytics Lessons From the Electronic Visualization Laboratory: A 25-Year Perspective

“…Many of the lessons we have learned reinforce the lessons learned from the War Room research of the 1990s, as well as recent research in collaborative non-immersive spaces. For example, Meade et al [11] also sought to bring a team of analysts together in a displayenhanced room for collaborative work, like our two case studies; although beyond that similarity, our case studies involve multi-disciplinary analysis work as a team, and require 3D and immersion for the sonar / bathymetry work, respectively for the halo spatial relationships. However, with almost all of our information living its entire life in a digital form, these lessons emphasize that the way we access and interact with information has changed.…”

Enabling Near Real-Time Remote Search for Fast Transient Events with Lossy Data Compression

“…To search for transient candidates in near real-time requires computational power that exceeds what is currently available on-site at CTIO. In this context, data files constantly need to be transmitted to a suitable location for post-processing, source finding, visualization and analysis (Meade et al, 2017;Andreoni et al, in press). The Green II supercomputer 1 at Swinburne University of Technology in Australia provides the computational power necessary for the main DWF goals.…”

“…The grand lines of the science pipeline are as follows. For detailed descriptions of the many DWF components, we refer the reader to Meade et al (2017); Andreoni et al (in press), and Cooke et al (in prep.). During the operational time of typical DWF run, three main steps are continuously being repeated for the optical data gathered by DECam:…”

“…(a) Visual analytics of potential candidates is performed by a group of experts and trained amateurs using an advanced visualisation facility (see Meade et al, 2017) and an online platform (database and other visualisation tools) 3 . (b) Provided that an interesting candidate is identified with sufficient confidence, a trigger is sent to the other telescopes for follow-up.…”

“…to be transmitted to a suitable location for post-processing, source finding, visualisation, and analysis (Meade et al 2017, Andreoni et al 2017. The Green II supercomputer 1 at Swinburne University of Technology in Australia provides the computational power necessary for the main DWF goals.…”