Keith Bechtol, Alex Drlica-Wagner, Kevork N. Abazajian, Muntazir Abidi, Susmita Adhikari, Yacine Ali-Haïmoud, James Annis, Behzad Ansarinejad, Robert Armstrong,… 展开作者
Keith Bechtol, Alex Drlica-Wagner, Kevork N. Abazajian, Muntazir Abidi, Susmita Adhikari, Yacine Ali-Haïmoud, James Annis, Behzad Ansarinejad, Robert Armstrong, Jacobo Asorey, Carlo Baccigalupi, Arka Banerjee, Nilanjan Banik, Charles Bennett, Florian Beutler, Simeon Bird, Simon Birrer, Rahul Biswas, Andrea Biviano, Jonathan Blazek, Kimberly K. Boddy, Ana Bonaca, Julian Borrill, Sownak Bose, Jo Bovy, Brenda Frye, Alyson M. Brooks, Matthew R. Buckley, Elizabeth Buckley-Geer, Esra Bulbul, Patricia R. Burchat, Cliff Burgess, Francesca Calore, Regina Caputo, Emanuele Castorina, Chihway Chang, George Chapline, Eric Charles, Xingang Chen, Douglas Clowe, Johann Cohen-Tanugi, Johan Comparat, Rupert A. C. Croft, Alessandro Cuoco, Francis-Yan Cyr-Racine, Guido D'Amico, Tamara M Davis, William A. Dawson, Axel de la Macorra, Eleonora Di Valentino, Ana Díaz Rivero, Seth Digel, Scott Dodelson, Olivier Doré, Cora Dvorkin, Christopher Eckner, John Ellison, Denis Erkal, Arya Farahi, Christopher D. Fassnacht, Pedro G. Ferreira, Brenna Flaugher, Simon Foreman, Oliver Friedrich, Joshua Frieman, Juan García-Bellido, Eric Gawiser, Martina Gerbino, Maurizio Giannotti, Mandeep S. S. Gill, Vera Gluscevic, Nathan Golovich, Satya Gontcho A Gontcho, Alma X. González-Morales, Daniel Grin, Daniel Gruen, Andrew P. Hearin, David Hendel, Yashar D. Hezaveh, Christopher M. Hirata, Renee Hložek, Shunsaku Horiuchi, Bhuvnesh Jain, M. James Jee, Tesla E. Jeltema, Marc Kamionkowski, Manoj Kaplinghat, Ryan E. Keeley, Charles R. Keeton, Rishi Khatri, Sergey E. Koposov, Savvas M. Koushiappas, Ely D. Kovetz, Ofer Lahav, Casey Lam, Chien-Hsiu Lee, Ting S. Li, Michele Liguori, Tongyan Lin, Mariangela Lisanti, Marilena LoVerde, Jessica R. Lu, Rachel Mandelbaum, Yao-Yuan Mao, Samuel D. McDermott, Mitch McNanna, Michael Medford, P. Daniel Meerburg, Manuel Meyer, Mehrdad Mirbabayi, Siddharth Mishra-Sharma, Moniez Marc, Surhud More, John Moustakas, Julian B. Muñoz, Simona Murgia, Adam D. Myers, Ethan O. Nadler, Lina Necib, Laura Newburgh, Jeffrey A. Newman, Brian Nord, Erfan Nourbakhsh, Eric Nuss, Paul O'Connor, Andrew B. Pace, Hamsa Padmanabhan, Antonella Palmese, Hiranya V. Peiris, Annika H. G. Peter, Francesco Piacentni, Francesco Piacentini, Andrés Plazas, Daniel A. Polin, Abhishek Prakash, Chanda Prescod-Weinstein, Justin I. Read, Steven Ritz, Brant E. Robertson, Benjamin Rose, Rogerio Rosenfeld, Graziano Rossi, Lado Samushia, Javier Sánchez, Miguel A. Sánchez-Conde, Emmanuel Schaan, Neelima Sehgal, Leonardo Senatore, Hee-Jong Seo, Arman Shafieloo, Huanyuan Shan, Nora Shipp, Joshua D. Simon, Sara Simon, Tracy R. Slatyer, Anže Slosar, Srivatsan Sridhar, Albert Stebbins, Oscar Straniero, Louis E. Strigari, Tim M. P. Tait, Erik Tollerud, M. A. Troxel, J. Anthony Tyson, Cora Uhlemann, L. Arturo Urenña-López, Aprajita Verma, Ricardo Vilalta, Christopher W. Walter, Mei-Yu Wang, Scott Watson, Risa H. Wechsler, David Wittman, Weishuang Xu, Brian Yanny, Sam Young, Hai-Bo Yu, Gabrijela Zaharijas, Andrew R. Zentner, Joe Zuntz
Astrophysical observations currently provide the only robust, empirical measurements of dark matter. In the coming decade, astrophysical observations will guide other experimental efforts, while simultaneously probing unique regions of dark matter parameter space. This white paper summarizes astrophysical observations that can constrain the fundamental physics of dark matter in the era of LSST. We describe how astrophysical observations will inform our understanding of the fundamental properties of dark matter, such as particle mass, self-interaction strength, non-gravitational interactions with the Standard Model, and compact object abundances. Additionally, we highlight theoretical work and experimental/observational facilities that will complement LSST to strengthen our understanding of the fundamental characteristics of dark matter.
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）
A Fully Photometric Approach to Type Ia Supernova Cosmology in the LSST Era: Host Galaxy Redshifts and Supernova Classification
National Center for Supercomputing Applications（国家超级计算应用中心）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Chicago（芝加哥大学）；Stanford University（斯坦福大学）；SLAC National Accelerator Laboratory（SLAC国家加速器实验室）；Massachusetts Institute of Technology（麻省理工学院）；Center for Astrophysics | Harvard & Smithsonian（天体物理中心|哈佛与史密森尼学会）；The Inter-University Centre for Astronomy and Astrophysics (IUCAA)（大学间天文学和天体物理学中心）
The Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC): Selection of a performance metric for classification probabilities balancing diverse science goals
Optimization of Weak Lensing Lightcone Simulations for Higher-Order Statistics in the LSST era
Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）；LPSC-IN2P3（格勒诺布尔粒子与核物理实验室）；Centre de Physique des Particules de Marseille（马赛粒子物理中心）；Aix Marseille Univ（艾克斯-马赛大学）；Newcastle University（纽卡斯尔大学）；Argonne National Laboratory（阿贡国家实验室）；Department of Physics, The University of Texas at Dallas（德克萨斯大学达拉斯分校物理系）；Universidad Nacional Autónoma de México（墨西哥国立自治大学）；Universität Bielefeld（比勒费尔德大学）
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。
Bayesian Neural Networks for classification tasks in the Rubin big data era
即将进行的调查，如维拉C。Rubin Observatory Legacy Survey of Space and Time（LSST）计划将在未来10年内，每晚探测到1000万个随时间变化的源。这些信息将以连续流的形式传输给经纪人，经纪人将使用机器学习算法为各种科学案例选择最有希望的事件。我们研究了贝叶斯神经网络（BNN）在这类分类任务中的优势和挑战。BNN被发现是准确的分类器，也提供了额外的信息：它们量化了分类的不确定性，可以利用这些不确定性来更有效地分析即将到来的数据雪崩。
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。
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）
