Elements: Bringing Montage To Cutting Edge Science Environments

天文学正在经历数据获取方式的转变，这也推动了天文学家处理这些数据的方式发生相应的转变。目前正在运行或将在未来几年开始运行的望远镜和天文观测将提供太大和太复杂的数据，无法通过将数据下载到桌面和本地集群的传统方法进行分析。因此，使用新技术处理数据的转型正在进行中。天文学家们接受了用于分析的Python语言，因为它提供了处理复杂数据所需的灵活构建块，并正在接受基于Python语言的新技术来管理和控制处理，使软件能够在数据本身旁边运行。蒙太奇图像马赛克引擎，一个已经被天文学家广泛使用的工具包，将加入这个变革性的社区，为天文学家和计算机科学家提供高性能的下一代图像处理能力。它将允许天文学家创建大规模的天空图像，并使用许多可用的强大工具来研究这些图像。Python已经成为天文学的首选语言，像JupyterLabs和JupyterHub这样的环境几乎肯定是未来的科学环境。LSST致力于将这样的环境用于其科学平台，这将是LSST用户发现、访问和分析数据的主要方式。天文科普档案馆也在积极搭建类似平台。NOAO已经部署了他们的Datalab，该数据库支持在Kitt Peak和CTIO获得的数据集。我们将把蒙太奇图像镶嵌引擎-一个用ANSI-C编写的可伸缩工具包，在天文学和信息技术中广泛使用-的功能整合到这样的环境中，以便在应用于大型和复杂的新数据集时发挥其全部功能。此外，相同的功能可以集成到单个桌面平台中，或集成到为支持新项目或任务而构建的新的可扩展环境中，或者集成到分布式可扩展环境中，如Amazon Elastic Cloud(将代码带到数据中)。作为一个基于组件的工具包，蒙太奇将能够很好地应对随着这些新平台的发展而预期的快速变化，并为了解它们的性能和可用性做出重大贡献。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.