Preparing for Science with the Rubin Observatory: Groundwork to Explore the Galactic Population of Exoplanets

由加州大学圣克鲁斯分校领导的一个研究小组将测试行星、星星和黑洞形成的想法，将预测结果与现实世界的测量结果进行比较。 维拉C。鲁宾天文台的时空遗产调查（LSST）将产生一系列突破性的发现。它将发现行星，恒星和黑洞在更大的距离在整个星系比以往任何时候都探测。但仅凭调查数据并不能完全描述这些发现。该项目将确保科学家们准备好实时响应LSST的发现。该团队将构建描述发现所需的软件，并了解行星，恒星和黑洞的人口。 他们开发的软件将公开提供给所有人。该团队将升级他们的免费教育网站，以帮助学生了解这些迷人的发现。表征快速演变的瞬态现象，如微透镜事件，需要一个苛刻的，快速响应的程序，以识别候选人从洪水的调查数据实时。该项目将建立必要的观测和理论基础设施，以描述Vera C. Rubin LSST，并将由此产生的工具提供给整个社区。该团队将扩展我们的算法，根据LSST等低节奏调查的发现警报实时优先处理微透镜事件，从而及时进行表征观察。他们将开发必要的分析和软件框架，以模拟鲁宾预期的大样本事件，了解选择偏差，并根据潜在人群解释检测到的事件样本。在此过程中，该团队将评估LSST调查策略，以确定额外的观测将产生最大的科学收益，从而优化我们的后续策略。这将是特别重要的协调鲁宾的调查策略，以补充南希罗马太空望远镜的同期系外行星调查。该团队的所有软件工具都将是开源的，并公开提供，以造福整个社区，该团队将扩展我们教育网站上的培训材料，使下一代学生更容易获得这一主题。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

A team led by the University of California-Santa Cruz will test ideas of planet, star and black hole formation to compare predictions with measurements of real-world discoveries. The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), will produce a groundbreaking set of discoveries. It will find planets, stars, and black holes at greater distances across the galaxy than has ever been probed before. But the survey data alone cannot fully characterize these discoveries. This project will make sure that scientists are ready to respond to LSST discoveries in real-time. The team will build the software needed to characterize the discoveries and understand the populations of planets, stars and black holes. The software they produce will be publicly available to benefit everyone. The team will upgrade their free educational website, to help students learn about these fascinating discoveries. Characterizing rapidly evolving transient phenomena like microlensing events requires a demanding, rapid-response program to identify candidates from the flood of survey data in real-time. This project will build the observational and theoretical infrastructure necessary to characterize a large sample of events discovered by the Vera C. Rubin LSST, and make the resulting tools available to the whole community. The team will extend our algorithm to prioritize microlensing events in real-time based on discovery alerts from low-cadence surveys like LSST, allowing characterization observations to be made in a timely manner. They will develop the analytical and software framework necessary to model the large sample of events expected from Rubin, understand the selection biases, and interpret the sample of detected events in terms of the underlying populations. In the process, the team will evaluate the LSST survey strategy to determine where additional observations will produce the greatest scientific yield and thereby optimize our follow-up strategy. This will be particularly important in coordinating Rubin's survey strategy to complement that of the Nancy Roman Space Telescope's contemporaneous exoplanet survey. All of the team's software tools will be open source and publicly available to benefit the whole community, and the team will extend the training materials available from our educational website to make this subject more accessible to the next generation of students.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.