Project Overview: Computational and experimental sciences produce and collect ever-larger and complex datasets, often in large-scale, multi-institution projects. The inability to gain insight into complex scientific phenomena using current software tools is a bottleneck facing virtually all endeavors of science. In order to address this problem for observational astrophysics, we built Sunfall, a collaborative visual analytics system for supernova discovery and data exploration.
Sunfall was developed for the Nearby Supernova Factory (SNfactory), 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.
Sunfall incorporates sophisticated astrophysics image processing algorithms, machine learning capabilities including boosted trees and support vector machines, and astronomical data analysis with a usable, highly interactive visual interface designed to facilitate collaborative decision making. An interdisciplinary group of physicists, astronomers, and computer scientists (with specialties in machine learning, visualization, and user interface design) were involved in all aspects of Sunfall design and implementation.
Researchers
Cecilia R. Aragon, Lawrence Berkeley National Lab, Berkeley, CA
Sarah S. Poon, Space Sciences Lab, Berkeley, CA
Rollin C. Thomas, Lawrence Berkeley National Lab, Berkeley, CA
Brian Lee, Lawrence Berkeley National Lab, Berkeley, CA
Stephen J. Bailey, Lawrence Berkeley National Lab, Berkeley, CA
Karl Runge, Space Sciences Lab, Berkeley, CA
Raquel A. Romano, Lawrence Berkeley National Lab, Berkeley, CA
Publications
Poon, S.S., Thomas, R.C., Aragon, C.R., and Lee, B. Context-linked virtual assistants for distributed teams: An astrophysics case study. In Proceedings of the 2008 ACM Conference on Computer Supported Cooperative Work, ACM (2008), 361-370. doi:10.1145/1460563.1460623 *Best Paper Award Nominee (one of 12 out of 235 submissions)
Aragon, C.R., Poon, S.S., Aldering, G.S., Thomas, R.C., and Quimby, R. Using visual analytics to maintain situational awareness in astrophysics. In Proceedings of the IEEE Symposium on Visual Analytics Science and Technology (IEEE VAST), IEEE (2008), 27-35. doi: 10.1109/VAST.2008.4677353
Aragon, C.R., Bailey, S.J., Poon, S., Runge, K., and Thomas, R.C. Sunfall: A Collaborative Visual Analytics System for Astrophysics. Journal of Physics Conference Series 125, 1 (2008). doi: 10.1088/1742-6596/125/1/012091 PDF
Bailey, S., Aragon, C., Romano, R., Thomas, R.C., Weaver, B.A., and Wong, D. Object classification at the nearby supernova factory. Astronomische Nachrichten 329, 3 (2008), 292-294. doi: 10.1002/asna.200710932 PDF
Aragon, C., and Poon, S. The impact of usability on supernova discovery. [Workshop on increasing the impact of usability work in software development]. CHI 2007: ACM Conference on Human Factors in Computing Systems, San Jose, CA (2007).
“The Impact of Usability on Supernova Discovery,” C. Aragon and S. Poon, LBNL-62380, Workshop on Increasing the Impact of Usability Work in Software Development,CHI 2007: ACM Conference on Human Factors in Computing Systems, San Jose, CA (2007).
Aragon, C., Bailey, S., Poon, S. Runge, K., and Thomas, R. Sunfall: A collaborative visual analytics system for astrophysics. [Poster]. IEEE Symposium on Visual Analytics Science and Technology 2007 (VAST 2007) , Sacramento, CA (2007). Poster PDF 2-page abstract PDF *Winner of Best Poster Award.
Aragon, C. and Aragon, D. B. A fast contour descriptor algorithm for supernova image classification, LBNL-61182. SPIE Symposium on Electronic Imaging: Real-Time Image Processing, San Jose, CA (2007). doi: 10.1117/12.703666 PDF
Bailey, S., Aragon, C., Romano, R., Thomas, R.C., Weaver, B.A., and Wong, D. How to find more supernovae with less work: Object classification techniques for difference imaging, LBNL-62659. Astrophysical Journal 665, 2 (2007), 1246-53.
Romano, R., Aragon, C., and Ding, C. Supernova recognition using support vector machines. In Proceedings of the 5th International Conference of Machine Learning Applications, IEEE (2006). doi:10.1109/ICMLA.2006.49 PDF *Winner of Best Application Paper Award.
