Visual Analytics for the Nearby Supernova Factory

Sunfall

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.


Supernova 1994D in the outskirts of the galaxy NGC 4526. This example of a type Ia supernova shows that at peak brightness they rival the cores of galaxies in luminosity (Hubble Space Telescope photo).

Overview

Many of today's important scientific breakthroughs are being made by large, interdisciplinary collaborations of scientists working in geographically widely distributed locations, producing and collecting vast and complex datasets. These large-scale science projects require software tools that support, not only insight into complex data, but collaborative science discovery. Visual analytics approaches, combining statistical algorithms and advanced analysis techniques with highly interactive visual interfaces that support collaborative work, offer scientists the opportunity for in-depth understanding of massive, noisy, and high-dimensional data.

Astrophysics in particular lends itself to a visual analytics approach due to the inherently visual nature of much astronomical data (including images and spectra). One of the grand challenges in astrophysics today is the effort to comprehend the mysterious "dark energy," which accounts for three-quarters of the matter/energy budget of the universe. The existence of dark energy may well require the development of new theories of physics and cosmology. Dark energy acts to accelerate the expansion of the universe (as opposed to gravity, which acts to decelerate the expansion). Our current understanding of dark energy comes primarily from the study of supernovae.

The Nearby Supernova Factory is an international astrophysics experiment designed to discover and measure Type Ia supernovae in greater number and detail than has ever been done before. These supernovae are stellar explosions that have a consistent maximum brightness, allowing them to be used as "standard candles" to measure distances to other galaxies and to trace the rate of expansion of the universe and how dark energy affects the structure of the cosmos. The SNfactory receives 50-80 GB of image data per night, which must be processed and examined by teams of domain experts within 12-24 hours to obtain maximum scientific benefit from the study of these rare and short-lived stellar events.


Supernova Warehouse DataTaking view from Sunfall (click on image for higher resolution view).

In order to facilitate the supernova search and data analysis process and enable scientific discovery for project astrophysicists, we developed Sunfall (SuperNova Factory AssembLy Line), a collaborative visual analytics system for the Nearby Supernova Factory that has been in production use for over a year. 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.

Publications