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

与鲁宾天文台一起为科学做准备：探索银河系外行星种群的基础工作

• 批准号: 2206828
• 负责人: Rachel Street
• 金额: $ 59.92万
• 依托单位: Las Cumbres Observatory Global Telescope Network
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2206828&HistoricalAwards=false
• 关键词: Preparing; Science; Rubin; Observatory; Groundwork

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.

由加州大学圣克鲁斯分校领导的团队将测试星球，星形和黑洞形成的想法，以将预测与现实发现的测量进行比较。 Vera C. Rubin天文台对时空的遗产调查（LSST）将产生一组开创性的发现。它将在整个银河系上的距离更远的地方找到行星，恒星和黑洞。但是，仅仅调查数据就无法完全表征这些发现。该项目将确保科学家准备实时回应LSST发现。该团队将构建描述发现并了解行星，恒星和黑洞的种群所需的软件。 他们生产的软件将公开使用，以使所有人受益。该团队将升级其免费的教育网站，以帮助学生了解这些有趣的发现。表征快速发展的瞬态现象（如微透镜事件）需要一个苛刻的快速响应计划，以实时从大量调查数据中识别候选者。该项目将建立表征Vera C. Rubin LSST发现的大量事件所需的观察和理论基础设施，并使所得工具可用于整个社区。该团队将扩展我们的算法，以根据LSST等低调调查的发现警报进行实时的微验证事件，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，以建模鲁宾预期的大量事件，了解选择偏见，并根据基础种群来解释检测事件的样本。在此过程中，团队将评估LSST调查策略，以确定其他观察结果将产生最大的科学收益率，从而优化我们的后续策略。这对于协调鲁宾的调查策略尤其重要，以补充南希罗马太空望远镜的同期系外行星的调查。团队的所有软件工具将是开源的，并公开可用以使整个社区受益，团队将扩展我们的教育网站可用的培训材料，以使下一代学生更容易获得该主题。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查的评估来通过评估来获得支持的。

项目成果

LSST Survey Strategy in the Galactic Plane and Magellanic Clouds

• DOI: 10.3847/1538-4365/acd6f4
• 发表时间: 2023-05
• 期刊: The Astrophysical Journal Supplement Series
• 作者: R. Street;X. Li;S. Khakpash;E. Bellm;L. Girardi;L. Jones;N. Abrams;Y. Tsapras;M. Hundertmark;E. Bachelet;P. Gandhi;P. Szkody;W. Clarkson;R. Szabo;L. Prisinzano;R. Bonito;D. Buckley;J. P. Marais;R. D. Stefano