Using visual analytics to maintain situation awareness in astrophysics

Creators: Aragon, Cecilia R.; Poon, Sarah S.; Aldering, Gregory S.; Thomas, Rollin C.; Quimby, Robert

Abstract

We present a novel collaborative visual analytics application for cognitively overloaded users in the astrophysics domain. The system was developed for scientists needing to analyze heterogeneous, complex data under time pressure, and then make predictions and time-critical decisions rapidly and correctly under a constant influx of changing data. The Sunfall Data Taking system utilizes several novel visualization and analysis techniques to enable a team of geographically distributed domain specialists to effectively and remotely maneuver a custom-built instrument under challenging operational conditions. Sunfall Data Taking has been in use for over eighteen months by a major international astrophysics collaboration (the largest data volume supernova search currently in operation), and has substantially improved the operational efficiency of its users. We describe the system design process by an interdisciplinary team, the system architecture, and the results of an informal usability evaluation of the production system by domain experts in the context of Endsley's three levels of situation awareness [1].

Additional Information

© 2008 IEEE. We would like to thank the anonymous reviewers for their thoughtful suggestions, and the scientists of the SNfactory collaboration for their time and detailed feedback. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Mauna Kea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain. This work was supported in part by the Director, Office of Science, Office of Advanced Scientific Computing Research, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, and by the Director, Office of Science, Office of High Energy Physics, of the U.S. Department of Energy under Contract No. DE-FG02-92ER40704, and by a grant from the Gordon & Betty Moore Foundation. This research used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.

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