Elements: FastTract: Web-Based Exploratory Visualization of Gigapixel Astronomical Images

American Astronomical Society, Washington DC

Investigators

Abstract

Like scientists in virtually every field, astronomers are struggling under a "data deluge". While the ever-larger images obtained by the newest observatories lead to cutting-edge science, they also overwhelm traditional tools designed to work with files hundreds or thousands of times smaller than the state-of-the-art. In particular, astronomers are rapidly losing the ability to simply look at the images of the sky that are coming out of their telescopes. The FastTract Project will solve this problem by marrying existing Web-based visualization technologies in the AAS WorldWide Telescope software system with new features and tools needed to efficiently work with the large astronomical images of the 2020's, enabling US astronomers to fully exploit their world-class facilities, in particular the ones that open new Windows on the Universe. The project team will synthesize the insight gained in this undertaking to establish the Little Big Data University (LBDU), a learning resource for scientists across disciplines who are struggling to stay afloat in the "data deluge". LBDU will follow the Khan Academy model and extensively use interactive environments to help researchers and others learn strategies for Harnessing the Data Revolution. These efforts will especially benefit people who do not have access to top-tier computational resources, such as those at small institutions and interested non-specialists. The FastTract Project will create a sustainable cyberinfrastructure (CI) system and associated community of practice that enable exploratory scientific visualization of large (gigapixel+) astronomical images over the Web. The CI will build on the NSF-funded AAS WorldWide Telescope (WWT) software system. The research team will extend WWT's image tiling architecture to work with FITS data files, develop the tooling needed for easy creation of such tiles, and produce workflows and tutorials allowing the broad astronomical community to take ownership of the infrastructure. The team will work with three NSF-funded partners on specific science applications. Annual workshops will nucleate a core group of project user-contributors and ensure that development effort meets the needs of the broader community. The project will adopt best practices in the open-source, open-development paradigm, laying the groundwork for FastTract infrastructure to be leveraged by non-astronomical communities facing similar data challenges. In parallel, the team will distill its expertise to create the Little Big Data University (LBDU), an online professional development resource aimed at domain scientists who do not intend to become CI specialists. These scientists will learn core strategies for coping with ever-larger data sets through the empirically successful Khan Academy model. Telemetry analysis and focus groups will guide curriculum design and assess efficacy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.