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arXiv 1708.04058astro-ph.IMastro-ph.COastro-ph.EPastro-ph.GAastro-ph.SR
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科学驱动的LSST观测策略优化

Science-Driven Optimization of the LSST Observing Strategy

LSST Science Collaboration, Phil Marshall, Timo Anguita, Federica B. Bianco, Eric C. Bellm, Niel Brandt, Will Clarkson, Andy Connolly, Eric Gawiser, Zeljko Ivez… 展开作者

LSST Science Collaboration, Phil Marshall, Timo Anguita, Federica B. Bianco, Eric C. Bellm, Niel Brandt, Will Clarkson, Andy Connolly, Eric Gawiser, Zeljko Ivezic, Lynne Jones, Michelle Lochner, Michael B. Lund, Ashish Mahabal, David Nidever, Knut Olsen, Stephen Ridgway, Jason Rhodes, Ohad Shemmer, David Trilling, Kathy Vivas, Lucianne Walkowicz, Beth Willman, Peter Yoachim, Scott Anderson, Pierre Antilogus, Ruth Angus, Iair Arcavi, Humna Awan, Rahul Biswas, Keaton J. Bell, David Bennett, Chris Britt, Derek Buzasi, Dana I. Casetti-Dinescu, Laura Chomiuk, Chuck Claver, Kem Cook, James Davenport, Victor Debattista, Seth Digel, Zoheyr Doctor, R. E. Firth, Ryan Foley, Wen-fai Fong, Lluis Galbany, Mark Giampapa, John E. Gizis, Melissa L. Graham, Carl Grillmair, Phillipe Gris, Zoltan Haiman, Patrick Hartigan, Suzanne Hawley, Renee Hlozek, Saurabh W. Jha, C. Johns-Krull, Shashi Kanbur, Vassiliki Kalogera, Vinay Kashyap, Vishal Kasliwal, Richard Kessler, Alex Kim, Peter Kurczynski, Ofer Lahav, Michael C. Liu, Alex Malz, Raffaella Margutti, Tom Matheson, Jason D. McEwen, Peregrine McGehee, Soren Meibom, Josh Meyers, Dave Monet, Eric Neilsen, Jeffrey Newman, Matt O'Dowd, Hiranya V. Peiris, Matthew T. Penny, Christina Peters, Radoslaw Poleski, Kara Ponder, Gordon Richards, Jeonghee Rho, David Rubin, Samuel Schmidt, Robert L. Schuhmann, Avi Shporer, Colin Slater, Nathan Smith, Marcelles Soares-Santos, Keivan Stassun, Jay Strader, Michael Strauss, Rachel Street, Christopher Stubbs, Mark Sullivan, Paula Szkody, Virginia Trimble, Tony Tyson, Miguel de Val-Borro, Stefano Valenti, Robert Wagoner, W. Michael Wood-Vasey, Bevin Ashley Zauderer

摘要:

大型综合巡天望远镜旨在提供前所未有的光学成像数据集,以支持对太阳系、银河系和宇宙的调查,跨越半个天空,进行十多年的重复观测。然而,究竟如何进行LSST观测(观测策略或“节奏”)尚未最终确定。在这份动态发展的社区白皮书中,我们探讨了预期科学调查的详细性能如何取决于LSST观测策略的微小变化。使用现实的模拟LSST的时间表和观测属性,我们设计和计算诊断指标和数字的优点,提供定量评估不同的观测策略,分析其影响范围广泛的拟议的科学项目。这是正在进行的工作:我们正利用这份白皮书,互相沟通可作出的观测策略选择的相对优点,以尽量发挥这项调查的科学价值。一些科学案例的调查导致了新的战略,可以模拟和潜在的采用建议。值得注意的是,我们发现的动机探索偏离空间均匀的年度平铺的天空:而不是集中在不同的调查区域的不同部分,在不同的年份在一个“滚动的节奏”很可能有显着的好处,一些时域和移动物体天文学项目。一套量化和均匀编码的指标的公共组合是对任何给定节奏模拟进行自动化,系统化,基于科学的评估的重要第一步,这将使LSST的调度尽可能充分知情。

英文摘要:

The Large Synoptic Survey Telescope is designed to provide an unprecedented optical imaging dataset that will support investigations of our Solar System, Galaxy and Universe, across half the sky and over ten years of repeated observation. However, exactly how the LSST observations will be taken (the observing strategy or "cadence") is not yet finalized. In this dynamically-evolving community white paper, we explore how the detailed performance of the anticipated science investigations is expected to depend on small changes to the LSST observing strategy. Using realistic simulations of the LSST schedule and observation properties, we design and compute diagnostic metrics and Figures of Merit that provide quantitative evaluations of different observing strategies, analyzing their impact on a wide range of proposed science projects. This is work in progress: we are using this white paper to communicate to each other the relative merits of the observing strategy choices that could be made, in an effort to maximize the scientific value of the survey. The investigation of some science cases leads to suggestions for new strategies that could be simulated and potentially adopted. Notably, we find motivation for exploring departures from a spatially uniform annual tiling of the sky: focusing instead on different parts of the survey area in different years in a "rolling cadence" is likely to have significant benefits for a number of time domain and moving object astronomy projects. The communal assembly of a suite of quantified and homogeneously coded metrics is the vital first step towards an automated, systematic, science-based assessment of any given cadence simulation, that will enable the scheduling of the LSST to be as well-informed as possible.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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