arXiv 1911.024792019-11-05astro-ph.IMcs.AI

PB时代科学发现的算法和统计模型

Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era

Fermi National Accelerator Laboratory（费米国家加速器实验室）；Kavli Institute of Cosmological Physics, University of Chicago（芝加哥大学卡弗里宇宙物理研究所）；University of Chicago（芝加哥大学）；University of Washington（华盛顿大学）；Google, Inc.（谷歌公司）；Space Telescope Science Institute（空间望远镜科学研究所）；The Johns Hopkins University（约翰斯·霍普金斯大学）；Carnegie Mellon University（卡内基梅隆大学）；Enrico Fermi Institute, The University of Chicago（芝加哥大学恩里科·费米研究所）；Center for Astrostatistics, Penn State University（宾夕法尼亚州立大学天体统计学中心）；Penn State University（宾夕法尼亚州立大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Center for Astrophysics, Harvard & Smithsonian（哈佛-史密森尼天体物理中心）；SETI Institute（搜寻地外文明研究所）

Brian Nord, Andrew J. Connolly, Jamie Kinney, Jeremy Kubica, Gautaum Narayan, Joshua E. G. Peek, Chad Schafer, Erik J. Tollerud, Camille Avestruz, G. Jogesh Bab… 展开作者

Brian Nord, Andrew J. Connolly, Jamie Kinney, Jeremy Kubica, Gautaum Narayan, Joshua E. G. Peek, Chad Schafer, Erik J. Tollerud, Camille Avestruz, G. Jogesh Babu, Simon Birrer, Douglas Burke, João Caldeira, Douglas A. Caldwell, Joleen K. Carlberg, Yen-Chi Chen, Chuanfei Dong, Eric D. Feigelson, V. Zach Golkhou, Vinay Kashyap, T. S. Li, Thomas Loredo, Luisa Lucie-Smith, Kaisey S. Mandel, J. R. Martínez-Galarza, Adam A. Miller, Priyamvada Natarajan, Michelle Ntampaka, Andy Ptak, David Rapetti, Lior Shamir, Aneta Siemiginowska, Brigitta M. Sipőcz, Arfon M. Smith, Nhan Tran, Ricardo Vilalta, Lucianne M. Walkowicz, John ZuHone

备注：: arXiv admin note: substantial text overlap with arXiv:1905.05116

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摘要：

天文学领域在数据集和科学合作的规模和复杂性方面已经达到了一个转折点。相应地，算法和统计模型已经开始适应-例如，通过人工智能的出现，这本身就为增长带来了新的挑战和机遇。本白皮书旨在为我们如何发展我们的技术和协作框架提供指导和想法，以促进有效的算法开发，并利用PB时代的科学发现机会。我们讨论了在大型复杂数据集中发现的挑战;统计方法和算法工具集开发的下一阶段的挑战和要求;我们如何改变我们的合作和教育范式;以及科学家对广泛适用的算法和计算建模的贡献的伦理影响。我们首先提出六项不同的建议，这些建议得到其后评注的支持。本白皮书与在Petabytes to Science研讨会（https：//petabytestoscience.github.io/）内部和周围进行的更大规模的工作相关。

英文摘要：

The field of astronomy has arrived at a turning point in terms of size and complexity of both datasets and scientific collaboration. Commensurately, algorithms and statistical models have begun to adapt --- e.g., via the onset of artificial intelligence --- which itself presents new challenges and opportunities for growth. This white paper aims to offer guidance and ideas for how we can evolve our technical and collaborative frameworks to promote efficient algorithmic development and take advantage of opportunities for scientific discovery in the petabyte era. We discuss challenges for discovery in large and complex data sets; challenges and requirements for the next stage of development of statistical methodologies and algorithmic tool sets; how we might change our paradigms of collaboration and education; and the ethical implications of scientists' contributions to widely applicable algorithms and computational modeling. We start with six distinct recommendations that are supported by the commentary following them. This white paper is related to a larger corpus of effort that has taken place within and around the Petabytes to Science Workshops (https://petabytestoscience.github.io/).

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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