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arXiv 1802.01783astro-ph.EPastro-ph.IM
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大型综合巡天望远镜太阳系科学路线图

Large Synoptic Survey Telescope Solar System Science Roadmap

Megan E. Schwamb, R. Lynne Jones, Steven R. Chesley, Alan Fitzsimmons, Wesley C. Fraser, Matthew J. Holman, Henry Hsieh, Darin Ragozzine, Cristina A. Thomas, Da… 展开作者

Megan E. Schwamb, R. Lynne Jones, Steven R. Chesley, Alan Fitzsimmons, Wesley C. Fraser, Matthew J. Holman, Henry Hsieh, Darin Ragozzine, Cristina A. Thomas, David E. Trilling, Michael E. Brown, Michele T. Bannister, Dennis Bodewits, Miguel de Val-Borro, David Gerdes, Mikael Granvik, Michael S. P. Kelley, Matthew M. Knight, Robert L. Seaman, Quan-Zhi Ye, Leslie A. Young

摘要:

大型综合巡天望远镜(LSST)由于其前所未有的深度和宽视场覆盖范围的组合,具有独特的搜索太阳系天体的能力。从2022年开始的十年期间,LSST将产生迄今为止最大的太阳系天体目录。LSST太阳系科学合作(SSSC)的主要目标是促进行星界利用LSST数据研究居住在我们太阳系内的行星和小天体种群的努力。为准备未来的调查节奏决定,并确保有趣和新颖的太阳系科学是可以实现的LSST,SSSC已确定和优先级的关键太阳系研究领域的调查与LSST在这个路线图。在这份动态文件中强调的科学优先事项将为LSST调查节奏决策提供信息,并有助于确定行星社区在计划开始LSST科学操作之前需要开发的软件工具和管道,作为附加值产品和资源。

英文摘要:

The Large Synoptic Survey Telescope (LSST) is uniquely equipped to search for Solar System bodies due to its unprecedented combination of depth and wide field coverage. Over a ten-year period starting in 2022, LSST will generate the largest catalog of Solar System objects to date. The main goal of the LSST Solar System Science Collaboration (SSSC) is to facilitate the efforts of the planetary community to study the planets and small body populations residing within our Solar System using LSST data. To prepare for future survey cadence decisions and ensure that interesting and novel Solar System science is achievable with LSST, the SSSC has identified and prioritized key Solar System research areas for investigation with LSST in this roadmap. The ranked science priorities highlighted in this living document will inform LSST survey cadence decisions and aid in identifying software tools and pipelines needed to be developed by the planetary community as added value products and resources before the planned start of LSST science operations.

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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