SI2-SSE: The Next Generation of the Montage Mosaic Engine

由新一代天文仪器产生的图像正在解决关于宇宙的基本问题，例如大爆炸后第一批星系的形成，以及我们银河系中恒星在大质量尘埃云中形成的最初阶段。开发这一新一代数据是困难的，因为它们产生的数据集足够复杂和庞大，需要新的数据处理方法，远远落后于仪器设备的发展。越来越多的社区正在努力纠正这种状况。该项目将提供软件工具，将来自新仪器的数据汇总成大范围天空区域的图像，以便天文学家能够充分研究上述确定的科学问题。这种研究聚合图像或马赛克的方法是天文学中的一个强大工具。该项目将交付下一代现有的马赛克建筑引擎，蒙太奇，它在天文学和教育活动中广泛使用。它将支持处理新的数据集，以便它们可以在万维网等身临其境的工具中可视化，该工具广泛用于开发创新的教育方法，并使它们能够生成供Zooniverse等公民科学服务使用的数据。蒙太奇将与一套工具捆绑在一起，使天文学家能够在强大的“云计算”平台上处理大量收集的图像。这些工具将适用于地震预测、DNA测序和气候建模等领域的许多数据密集型问题。最后，蒙太奇在开发和测试国家网络基础设施方面得到了广泛的使用，使美国科学界受益。我们预计，随着所有领域的数据量快速增长，下一代蒙太奇将被以同样的方式用于开发更强大的网络基础设施。更详细地说，该项目将提供下一代蒙太奇图像马赛克引擎，该引擎将提供新的功能，以响应不断变化的天文数据和计算地形。应用户社区的要求，这些功能是：1.支持数据立方体的马赛克，现在通常由现代工具生成；2.支持两种广泛使用的天空分区方案，HEALPix和Toast；3.允许用户用Python和其他语言直接调用Monage的API。开发内存管理和子集技术以支持马赛克的工作将可供其他人使用和扩展。对HEALPix的支持将使远红外、宇宙背景数据集与其他图像数据集能够集成和分析。Toast基本上将使任何图像数据集都可以合并到WWT中。蒙太奇将与一整套开放源代码工具捆绑在一起，这些工具可以在云平台上配置资源和运行应用程序。该包将建立在使用云平台大规模创建数据产品所获得的知识基础上。这些工具将为缺乏系统配置知识的科学家带来云计算，这是进入的最大障碍之一；这些工具是通用的，将适用于5月领域的数据密集型应用。因此，蒙太奇将为天文学家、大规模分析数据的项目创造新的数据产品以及天文学以外的数据密集型领域的科学家提供强大的新能力。下一代工具包将继承可持续蒙太奇架构，该架构已经吸引了天文学家、E/PO专家和计算机技术专家的大量用户基础。蒙太奇是用C语言编写的，可以在所有常见的Unix平台上移植，高度可伸缩，并以组件的形式提供，易于整合到管道和处理环境中。蒙太奇是唯一具有所有这些特征的马赛克引擎。该项目将使用渐进式交付生命周期模型。这些代码将在GitHub存储库上可供访问，并以开放源代码的形式发布，并带有BSD 3条款许可证。用户小组将就详细规格提供建议。

Images produced by the new generation of astronomical instruments are addressing fundamental questions about the Universe, such as the formation of the very first galaxies after the Big Bang, and the very first stages of the formation of stars in massive dust clouds in our Galaxy. Exploiting this new generation of data is difficult because the data sets they produce are sufficiently complex and large as to demand new approaches to data processing that lag far behind developments in instrumentation. A growing community is working to rectify this state-of-affairs. This project will deliver software tools that will aggregate data from the new instruments into images of large scale regions of the sky so that astronomers can fully study scientific questions such as those identified above. This approach of studying aggregated images, or mosaics, is a powerful tool in astronomy. The project will deliver the next generation of an existing mosaic-building engine, Montage, which is in wide use in astronomy and in educational activities. It will support processing of the new data sets such that they can be visualized in immersive tools such as the World Wide Telescope, widely used in developing innovative approaches to education, and such that that they can generate data used by Citizen Science services such as Zooniverse. Montage will come be bundled with a set of tools that will enable astronomers to process massive collections images on powerful "cloud computing" platforms. These tools will be applicable to many data-intensive problems in fields such as earthquake prediction, DNA sequencing, and climate modeling. Finally, Montage is in wide use in developing and testing national cyberinfrastructure to benefit the U.S. science community. We anticipate that the next-generation Montage will be used in the same way to develop ever more powerful cyberinfrastructure as data volumes grow rapidly in all fields.In greater detail, the project will deliver the next generation of the Montage image mosaic engine, which will offer new capabilities that respond to the changing astronomy data and computing landscapes. These capabilities, requested by the user community, are: 1. Support for mosaicking of data cubes, now routinely generated by modern instrumentation; 2. Support for two widely used sky-partitioning schemes, HEALPix and TOAST; 3. An API to enable users to call Montage directly in Python and other languages. The work to develop memory management and subsetting techniques to support mosaicking will be available for others to use and extend. Support for HEALPix will enable integration and analysis of far-infrared, cosmic background data sets with other image data sets. TOAST will enable essentially any image data set to be incorporated into the WWT. Montage will be bundled with a turnkey package of open source tools that provision resources and run applications on cloud platforms. This package will build on knowledge gained in creating data products at scale with cloud platforms. These tools will bring cloud computing to scientists who have little system configuration knowledge, one of the biggest barriers to entry; these tools are general purpose and will be applicable to data intensive applications in may fields. Thus Montage will provide powerful new capabilities to astronomers, to projects analyzing data at scale to create new data products, and to scientists in data-intensive fields outside astronomy. The next-generation toolkit will inherit the sustainable Montage architecture, which has attracted a large user base among astronomers, E/PO specialists, and computer technologists. Montage is written in C, is portable across all common Unix platforms, highly scalable and delivered as components that are easy to incorporate into pipelines and processing environments. Montage is the only mosaic engine with all these characteristics. The project will use the evolutionary delivery lifecycle model. The code will be a made accessible on the GitHub repository, and released as Open Source code with a BSD 3-clause license. A Users' Panel will advise on detailed specifications.