Collaborative Analytics for Astrophysics Explorations

Abstract

Many of todayʼs important scientific breakthroughs are made by large, interdisciplinary collaborations of scientists working in geographically distributed locations, producing and collecting vast and complex datasets. Experimental astrophysics, in particular, has recently become a data-intensive science after many decades of relative data poverty. These large-scale science projects require software tools that support not only insight into complex data but collaborative science discovery. Such projects do not easily lend themselves to fully automated solutions, requiring hybrid human–automation systems that facilitate scientist input at key points throughout the data analysis and scientific discovery process. This chapter presents some of the issues to consider when developing such software tools, and describes Sunfall, a collaborative visual analytics system developed for the Nearby Supernova Factory, an international astrophysics experiment and the largest data volume supernova search currently in operation. Sunfall utilizes novel interactive visualization and analysis techniques to facilitate deeper scientific insight into complex, noisy, high-dimensional, high-volume, time-critical data. The system combines novel image-processing algorithms, statistical analysis, and machine learning with highly interactive visual interfaces to enable collaborative, user-driven scientific exploration of supernova image and spectral data. Sunfall is currently in operation at the Nearby Supernova Factory; it is the first visual analytics system in production use at a major astrophysics project. The chapter concludes with a set of guidelines and lessons learned about developing software to support scientific collaborations.