Elements: Bringing Montage To Cutting Edge Science Environments

California Institute Of Technology, Pasadena CA

Investigators

Abstract

Astronomy is undergoing a transformation in the way data are acquired, and this is driving a corresponding transformation in the way astronomers process these data. Telescopes and sky surveys that are operating now or will begin to operate in the coming years will deliver data that are too large and complex to analyze by the traditional method of downloading data to desktops and local clusters. Thus a transformation is underway to use new technologies to process data. Astronomers have embraced the Python language for analysis because it provides the necessary flexible building blocks to handle complex data, and are embracing new Python-based technologies to manage and control processing that allows the software to run next to the data themselves. The Montage image mosaic engine, a toolkit already used widely by astronomers, will join this transformative community and deliver high-performance, next generation image processing capabilities for astronomers and computer scientists. It will allow astronomers to create large-scale images of the sky, and study these images with the many powerful tools available in Python. Python has become the language of choice for astronomy, and environments such as JupyterLabs and JupyterHub are almost certainly the science environments of the future. The LSST is committed to using such an environment for its science platform, which will be the primary way LSST users will discover, access and analyze data. Astronomy science archives are actively building similar platforms. NOAO has deployed their DataLab, which supports datasets acquired at Kitt Peak and CTIO. We will incorporate the functionality of the Montage image mosaic engine - a scalable toolkit written in ANSI-C and in wide use in astronomy and information technology - into environments such as these to unleash its full power when applied to large and complex new datasets. Moreover, the same functionality can be incorporated into a single desktop platform, or into a new scalable environment built to support a new project or mission, or into a distributed scalable environment such the Amazon Elastic Cloud ("bringing the code to the data"). As a component-based toolkit, Montage will be well positioned to respond to the rapid changes expected as these new platforms develop and contribute substantially to understanding their performance and usefulness. 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.