论文标题
从数据到软件再到科学与鲁宾天文台LSST
From Data to Software to Science with the Rubin Observatory LSST
论文作者
论文摘要
Vera C. Rubin守护现象的遗产调查（LSST）数据集将极大地改变我们对宇宙的理解，从太阳系的起源到暗物质和暗能量的本质。这项研究的大部分将取决于强大，测试和可扩展算法，软件和服务的存在。提前确定和开发此类工具有可能显着加速从LSST提供早期科学的能力。协作开发这些，并使其广泛可用，可以使LSST科学方面更具包容性和公平的合作。 为了促进这种机会，LSST跨学科的合作与计算机网络（LINCC）和合作伙伴组织了一个有效的社区研讨会，该社区研讨会是“从数据到软件再到科学”，并在纽约的Flatiron Institute举行，3月28日至30日，在纽约的Flatiron Institute举行，2022年3月28日至30日。它确定了七个关键需求领域：（i）目录的可扩展交叉匹配和分布式连接，（ii）可稳健的光度红移确定，（iii）确定选择功能的软件，（iiv）可扩展时间序列分析的框架，（iv）可伸缩时间序列分析的框架，（v）在图像上访问和（v）在规模上和（VI）的（vi）式（vi）的（VI）式（vi）的（vi）对象（vi），（VI）对象（vi）对象（VI），（VI）对象（VI）对象（vi）对象（VI）对象（VI），则（VI）对象（VI）对象（VI）对象（VI）的（VI），VI）。系统。 这份白皮书总结了该研讨会的讨论。它考虑了激励人心的科学用例，确定了交叉切割算法，软件和服务，它们的高级技术规格以及开发它们所需的包容性合作原则。我们将其作为需求的有用路线图，并刺激了群体和希望为早期LSST科学开发可重复使用的软件的团体和个人之间的协作。
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) dataset will dramatically alter our understanding of the Universe, from the origins of the Solar System to the nature of dark matter and dark energy. Much of this research will depend on the existence of robust, tested, and scalable algorithms, software, and services. Identifying and developing such tools ahead of time has the potential to significantly accelerate the delivery of early science from LSST. Developing these collaboratively, and making them broadly available, can enable more inclusive and equitable collaboration on LSST science. To facilitate such opportunities, a community workshop entitled "From Data to Software to Science with the Rubin Observatory LSST" was organized by the LSST Interdisciplinary Network for Collaboration and Computing (LINCC) and partners, and held at the Flatiron Institute in New York, March 28-30th 2022. The workshop included over 50 in-person attendees invited from over 300 applications. It identified seven key software areas of need: (i) scalable cross-matching and distributed joining of catalogs, (ii) robust photometric redshift determination, (iii) software for determination of selection functions, (iv) frameworks for scalable time-series analyses, (v) services for image access and reprocessing at scale, (vi) object image access (cutouts) and analysis at scale, and (vii) scalable job execution systems. This white paper summarizes the discussions of this workshop. It considers the motivating science use cases, identified cross-cutting algorithms, software, and services, their high-level technical specifications, and the principles of inclusive collaborations needed to develop them. We provide it as a useful roadmap of needs, as well as to spur action and collaboration between groups and individuals looking to develop reusable software for early LSST science.
