Realizing the Promise of LSST Combined Probes of Dark Energy

大型天文观测望远镜(LSST)计划的最重要项目之一将是测量暗能量的宇宙效应。这些效应将通过它们对早期宇宙结构增长的影响以及对星系和星系团的弱引力透镜的影响来检测。为了使这些努力取得成功，现在就应该开始研究潜在的误差源。LSST调查预计将包括10亿个数量级的星系，统计误差如此之小，以至于系统误差--最重要的是在光度学红移和弱引力透镜剪切的测量中--可能占主导地位。在这个项目中，提议团队将对暗能量测量中这些系统效应的来源和影响进行全面研究，并开发算法、分析工具和观测技术，将其影响降至最低。他们将利用对剩余系统学更好的理解来模拟LSST的性能，作为建造的先导。他们之前已经发现，暗能量约束对系统学很敏感，这在很大程度上取决于单独的技术(如弱透镜、宇宙剪切和重子声振荡)是如何组合的。该团队打算利用这种敏感性，并通过以最佳方式组合几个观测探测器来研究控制系统误差的新方法。他们将使用LSST的端到端模拟来开发和测试这些算法，并将它们应用于深度透镜调查(DLS)的当前数据，以在短期内产生科学成果。该团队打算将其技术、数据和开源软件公开提供，没有专利期，并期望他们的光度学红移算法将得到广泛应用。除了通过指导一名博士后研究人员为教育做出贡献外，该项目还将通过合作开发新的工具来可视化宇宙的黑暗和光明结构，从而参与公共宣传和非正式教育。

Among the most important projects planned for the Large Synoptic Survey Telescope (LSST) will be the measurement of the cosmological effects of dark energy. These effects will be detected through their influence on the growth of structure in the early universe and on weak gravitational lensing by galaxies and galaxy clusters. In order for these efforts to succeed, studies of the potential sources of error should begin now. The LSST survey, expected to comprise of order 1 billion galaxies, will have such small statistical errors that systematic errors - the most important being in the measurement of photometric redshift and weak gravitational lens shear - may dominate. In this project, the proposing team will carry out a comprehensive study of the origin and impact of these systematic effects in dark energy measurements, and develop algorithms, analysis tools and observing techniques to minimize their impact. They will use the improved understanding of the residual systematics to simulate the performance of LSST, as a precursor to construction. They have previously found that dark energy constraints are sensitive to systematics in ways that depend strongly on how separate techniques (such as weak lensing, cosmic shear, and baryon acoustic oscillations) are combined. The team intends to exploit this sensitivity and investigate new methods for controlling systematic error by combining several observational probes in an optimal way. They will use end-to- end simulations of LSST to develop and test these algorithms, and apply them to the current data from the Deep Lens Survey (DLS) to produce science in the near term. The team intends to make its techniques, data, and open-source software publicly available with no proprietary period, with the expectation that their photometric redshift algorithm will be widely used. In addition to contributing to education through the mentoring of a postdoctoral researcher, the project will also engage in public outreach and informal education by partnering in the development of new tools to visualize the dark and luminous structure of the Universe.