The LSST Dark Energy Science Collaboration (DESC) Science Requirements Document
The LSST Dark Energy Science Collaboration, Rachel Mandelbaum, Tim Eifler, Renée Hložek, Thomas Collett, Eric Gawiser, Daniel Scolnic, David Alonso, Humna Awan,… 展开作者
The LSST Dark Energy Science Collaboration, Rachel Mandelbaum, Tim Eifler, Renée Hložek, Thomas Collett, Eric Gawiser, Daniel Scolnic, David Alonso, Humna Awan, Rahul Biswas, Jonathan Blazek, Patricia Burchat, Nora Elisa Chisari, Ian Dell'Antonio, Seth Digel, Josh Frieman, Daniel A. Goldstein, Isobel Hook, Željko Ivezić, Steven M. Kahn, Sowmya Kamath, David Kirkby, Thomas Kitching, Elisabeth Krause, Pierre-François Leget, Philip J. Marshall, Joshua Meyers, Hironao Miyatake, Jeffrey A. Newman, Robert Nichol, Eli Rykoff, F. Javier Sanchez, Anže Slosar, Mark Sullivan, M. A. Troxel
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32 pages + 60 pages of appendices. This is v1.0.2 of the DESC SRD, an internal collaboration document that is being made public and is not planned for submission to a journal. Data products for reproducing key plots are available on Zenodo, https://doi.org/10.5281/zenodo.1409815 ; see "Executive Summary and User Guide" for how to use and cite those products
The Large Synoptic Survey Telescope (LSST) Dark Energy Science Collaboration (DESC) will use five cosmological probes: galaxy clusters, large scale structure, supernovae, strong lensing, and weak lensing. This Science Requirements Document (SRD) quantifies the expected dark energy constraining power of these probes individually and together, with conservative assumptions about analysis methodology and follow-up observational resources based on our current understanding and the expected evolution within the field in the coming years. We then define requirements on analysis pipelines that will enable us to achieve our goal of carrying out a dark energy analysis consistent with the Dark Energy Task Force definition of a Stage IV dark energy experiment. This is achieved through a forecasting process that incorporates the flowdown to detailed requirements on multiple sources of systematic uncertainty. Future versions of this document will include evolution in our software capabilities and analysis plans along with updates to the LSST survey strategy.
Extracting intrinsic alignments in the Dark Energy Survey's year 1 data, using the self-calibration method and LSST-DESC tools
Newcastle University（纽卡斯尔大学）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）；Harvard University（哈佛大学）；Duke University（杜克大学）；University of Edinburgh（爱丁堡大学）；University of Chicago（芝加哥大学）
A Joint Roman Space Telescope and Rubin Observatory Synthetic Wide-Field Imaging Survey
我们提出并验证了20度$^2$的重叠合成成像调查，代表了南希格雷斯罗马空间望远镜高纬度成像调查（HLIS）的全深度和五年的观察维拉C。Rubin Observatory Legacy Survey of Space and Time（鲁宾天文台时空遗产巡天）这两个综合调查进行了总结，参考现有的300度$^2$的LSST模拟成像的一部分，暗能量科学合作（DESC）数据挑战2（DC 2）。这两次合成巡天观测到的是同一个模拟的DESC DC 2宇宙。对于合成罗马调查，我们首次模拟了全彩色图像以及使用飞行探测器从实验室测量中获得的传感器芯片组件的详细物理特性。模拟成像和所得到的物体光度特性的像素级测量跨越$\sim$ 0.3至2.0 $μ$ m的波长范围。我们还描述了罗马模拟管道的更新，相对于原始DC 2模拟的天体物理对象模拟方式的变化，以及由此产生的模拟罗马数据产品。我们使用这些模拟来探索LSST图像中未识别的混合物的相对比例，发现在LSST图像中识别的$i$波段亮度大于25的对象中有20-30%可以被识别为罗马图像中的多个对象。这些模拟为2020年代后半期开发和验证地基和天基成像数据集的像素级联合分析技术-特别是Roman-LSST联合分析-提供了一个独特的试验场。
The Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC): Selection of a performance metric for classification probabilities balancing diverse science goals
Probabilistic characterization of blending with LSST and application to cluster lensing cosmology
University of Cincinnati（辛辛那提大学）；LPSC-IN2P3, Univ. Grenoble Alpes, CNRS（格勒诺布尔阿尔卑斯大学IN2P3粒子与核物理实验室CNRS）；Istituto Nazionale di Astrofisica（意大利国家天体物理研究所）；Stanford University（斯坦福大学）；Université Paris-Saclay, CEA, IRFU（巴黎-萨克雷大学CEA法国原子能和替代能源委员会IRFU）；University of Michigan（密歇根大学）；Space Telescope Science Institute（太空望远镜科学研究所）；Université Paris Cité, CNRS/IN2P3, APC（巴黎西岱大学CNRS/IN2P3APC实验室）；Duke University（杜克大学）
Redshift Assessment Infrastructure Layers (RAIL): Rubin-era photometric redshift stress-testing and at-scale production
Ruhr University Bochum（鲁尔大学波鸿）；SLAC National Accelerator Laboratory（斯坦福直线加速器中心）；Kavli Institute for Particle Astrophysics and Cosmology (KIPAC), Stanford University（卡弗里粒子天体物理与宇宙学研究所，斯坦福大学）；Université Clermont-Auvergne, CNRS, LPCA（克莱蒙奥弗涅大学，法国国家科学研究中心，LPCA）；McWilliams Center for Cosmology and Astrophysics, Department of Physics, Carnegie Mellon University（梅斯威尔宇宙学与天体物理中心，卡内基梅隆大学物理系）
RAIL 是一个开源 Python 库，用于大规模概率光度红移估计，提供模块化工具进行端到端压力测试和红移 PDF 估算，适用于多种科学目标。
Recommended Target Fields for Commissioning the Vera C. Rubin Observatory
Vera C的调试团队。鲁宾天文台正在计划一套工程和科学验证观测与遗产调查的空间和时间（LSST）调试相机，然后鲁宾天文台LSST相机。这些观测的时间框架尚未确定，委托小组将灵活选择观测领域。在本文件中，暗能量科学合作（DESC）委托工作组提出了一个优先的目标领域清单，适合测试DESC相关科学性能的各个方面，按季节分组，以便从Cerro Pachon的鲁宾天文台获得可见度。我们推荐的字段包括用于照片- $z$和形状校准目的的深度钻探字段（DDFs）到完整的LSST深度，用于去混合研究的HST成像字段到完整的深度，以及几个过滤器中的$\sim$ 200平方度区域到1年深度，用于DESC广域科学案例的更高级别验证。我们还预计，将需要委托观测的模板建设瞬态科学在广泛的RA范围。我们包括我们推荐的字段的详细描述以及相关的参考资料。我们乐观地认为，这份文件将继续在LSST操作中发挥作用，因为它提供了一个全面的重叠数据集列表和描述它们的参考文献。
Uniform Rolling: An LSST Observing Cadence Offering Sufficient Survey Uniformity for Comprehensive Cosmological Analysis
Imperial College London（帝国理工学院）；Argonne National Laboratory（阿贡国家实验室）；Stanford University（斯坦福大学）；SLAC National Accelerator Laboratory（斯坦福直线加速器中心）；Rutgers, the State University of New Jersey（新泽西州立罗格斯大学）；University College London（伦敦大学学院）；University of Toronto（多伦多大学）；Duke University（杜克大学）；University of the Western Cape（西开普敦大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Pittsburgh（匹兹堡大学）；University of Cambridge（剑桥大学）；Stockholm University（斯德哥尔摩大学）；University of Washington（华盛顿大学）
Halo Occupation Distribution estimation performance for LSST data
Instituto de Astronomía y Física del Espacio, CONICET-UBA（空间物理与天文学研究所，CONICET-UBA）；Instituto de Astronomía Teórica y Experimental - Observatorio Astronómico de Córdoba (IATE, UNC–CONICET)（理论与实验天文学研究所 - 科尔多瓦天文台 (IATE, UNC–CONICET)）；Observatorio Astronómico de Córdoba, Universidad Nacional de Córdoba（科尔多瓦天文台，科尔多瓦国立大学）；Facultad de Matemática, Astronomía y Física, Universidad Nacional de Córdoba (FaMAF–UNC)（数学、天文学与物理学院，科尔多瓦国立大学 (FaMAF–UNC)）；Instituto de Astronomia, Universidad Nacional Autonoma de Mexico（天文学研究所，墨西哥国立自治大学）；Center for Theoretical Physics, Polish Academy of Sciences（波兰科学院理论物理中心）；Universidad Andres Bello, Facultad de Ciencias Exactas, Departamento de Fisica y Astronomia, Instituto de Astrofisica（安德烈斯贝略大学，理学院，物理与天文学系，天体物理研究所）
Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration
Université Paris Cité, CNRS, CEA, Astroparticule et Cosmologie, F-75013 Paris, France；Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA；Leinweber Institute of Theoretical Physics, University of Michigan, Ann Arbor, MI 48109, USA；Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA；Cavendish Astrophysics, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK；Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK；SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA；Department of Computer Science, University of Milan, Milan, Italy；Université Paris Cité, CNRS, Astroparticule et Cosmologie, F-75013 Paris, France；Université Paris-Saclay, CNRS/IN2P3, IJCLab, 91405 Orsay, France；Department of Astronomy；Astrophysics, University of Chicago, Chicago, IL 60637, USA；Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637, USA；NSF-Simons AI Institute for the Sky (SkAI), 172 E. Chestnut St., Chicago, IL 60611, USA；Fermi National Accelerator Laboratory, P.O. Box 500, Batavia, IL 60510, USA；Universit\'e Clermont-Auvergne, CNRS, LPCA, 63000 Clermont-Ferrand, France；Kavli Institute for Particle Astrophysics；Cosmology, Stanford University, Stanford, CA 94305, USA；Department of Physics, Stanford University, 382 Via Pueblo Mall, Stanford, CA 94305, USA；Engineering Faculty, Universidad Autonoma de San Luis Potosi, Zona Universitaria, San Luis Potosi, 78290, Mexico；Stanford Artificial Intelligence Laboratory, Stanford University, Stanford, CA 94305, USA；Kavli Institute of Cosmological Physics, University of Chicago, Chicago, IL 60637, USA；The NSF AI Institute for Artificial Intelligence；Center for Astrophysics Harvard \& Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA；Department of Physics；Kavli Institute for Astrophysics；Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA；Institut de Física d'Altes Energies (IFAE), The Barcelona Institute of Science；Institute of Astronomy；Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge, CB3 0HA, UK；Imperial Centre for Inference；Cosmology (ICIC), Imperial College London, Blackett Laboratory, Prince Consort Road, London SW7 2AZ, UK；Data Science Institute, The University of Chicago, Chicago, IL 60615, USA；Department of Physics, The University of Texas at Dallas, Richardson, TX 75080, USA；Department of Physics, Duke University, Durham, NC 27708, USA；Université Paris-Saclay, Université Paris Cité, CEA, CNRS, AIM, F-91191 Gif-sur-Yvette, France；School of Mathematics, Statistics；Physics, Newcastle University, Newcastle upon Tyne, NE1 7RU, United Kingdom；Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544, USA；Astronomy, University of the Western Cape, Bellville, Cape Town, 7535, South Africa；Astronomy, University of Utah, Salt Lake City, UT 84112, USA；Department of Astrophysical Sciences, Princeton University, Peyton Hall, Princeton, NJ 08544, USA；Department of Astronomy, University of Illinois Urbana Champaign, 1002 W. Green St., Urbana, IL, 61801, USA；Institute for Particle Physics；Astrophysics, ETH Zürich, Wolfgang-Pauli-Strasse 27, CH-8093 Zurich, Switzerland；Swinburne University of Technology, Hawthorn, Victoria 3122, Australia；Ciela - Montr\'eal Institute for Astrophysical Data Analysis；Mila - Quebec Artificial Intelligence Institute, Montréal, QC H2S 3H1, Canada；Advanced Research Computing Centre, University College London, 90 High Holborn, London WC1V 6LJ, UK；Finnish Centre for Astronomy with ESO (FINCA), University of Turku, FI-20014 Turku, Finland；Department of Physics, P.O. Box 64, University of Helsinki, FI-00014 Helsinki, Finland；Astronomy, Northwestern University, Evanston, IL, USA；Center for Interdisciplinary Exploration；Research in Astrophysics, Northwestern University, Evanston, IL, USA；Scientific Data Science, International School for Advanced Study, Via Bonomea 265, I-34136 Trieste, Italy；Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA；PITT PACC, University of Pittsburgh, Pittsburgh, PA 15260, USA；NSF NOIRLab, 950 N. Cherry Ave., Tucson, AZ 85719, USA
Direct geometrical measurement of the Hubble constant from galaxy parallax: predictions for the Vera C. Rubin Observatory and Nancy Grace Roman Space Telescope
