Collaborative Research: Scalable CyberInfrastructure for Artificial Intelligence and Likelihood Free Inference (SCAILFIN)

合作研究：用于人工智能和似然自由推理的可扩展网络基础设施 (SCAILFIN)

• 批准号: 1841471
• 负责人: Kyle Cranmer
• 金额: $ 48.69万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1841471&HistoricalAwards=false
• 关键词: Collaborative; Research; Scalable; CyberInfrastructure; Artificial

The National Science Foundation (NSF) has made significant investments in major multi-user research facilities (MMURFs), which are the foundation for a robust data-intensive science program. Extracting scientific results from these facilities involves the comparison of "real" data collected from the experiments with "synthetic" data produced from computer simulations. There is wide growing interest in using new machine learning (ML) and artificial intelligence (AI) techniques to improve the analysis of data from these facilities and improve the efficiency of the simulations. The SCAILFIN project will use recently developed algorithms and computing technologies to bring cutting-edge data analysis techniques to such facilities, starting with the data from the international Large Hadron Collider. One result of these advancements will be that research groups at smaller academic institutions will more easily be able to access to the necessary computing resources which are often only available at larger institutions. Removing access barriers to such resources democratizes them, which is key to developing a diverse workforce. This effort will also contribute to workforce development through alignment of high-energy physics data analysis tools with industry computing standards and by training students in high-value data science skills.The main goal of the SCAILFIN project is to deploy artificial intelligence and likelihood-free inference (LFI) techniques and software using scalable cyberinfrastructure (CI) that is developed to be integrated into existing CI elements, such as the REANA system. The analysis of LHC data is the project's primary science driver, yet the technology is sufficiently generic to be widely applicable. The LHC experiments generate tens of petabytes of data annually and processing, analyzing, and sharing the data with thousands of physicists around the world is an enormous challenge. To translate the observed data into insights about fundamental physics, the important quantum mechanical processes and response of the detector to them need to be simulated to a high-level of detail and accuracy. Investments in scalable CI that empower scientists to employ ML approaches to overcome the challenges inherent in data-intensive science such as simulation-informed inference will increase the discovery reach of these experiments. The development of the proposed scalable CI components will catalyze convergent research because 1) the abstract LFI problem formulation has already demonstrated itself to be the "lingua franca" for a diverse range of scientific problems; 2) the current tools for many tasks are limited by lack of scalability for data-intensive problems with computationally-intensive simulators; 3) the tools the project is developing are designed to be scalable and immediately deployable on a diverse set of computing resources due to the design; and 4) the integration of additional commonly-used workflow languages to drive the optimization of ML components and to orchestrate large-scale workflows will lower the barrier-to-entry for researchers from other domains.This project is supported by the Office of Advanced Cyberinfrastructure in the Directorate for Computer and Information Science and Engineering.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

美国国家科学基金会(NSF)在主要的多用户研究设施(MMURF)上进行了大量投资，这些设施是强大的数据密集型科学计划的基础。从这些设施中提取科学结果涉及到将从实验中收集的“真实”数据与从计算机模拟产生的“合成”数据进行比较。人们对使用新的机器学习(ML)和人工智能(AI)技术来改进来自这些设施的数据分析和提高模拟的效率越来越感兴趣。SCAILFIN项目将使用最近开发的算法和计算技术，从国际大型强子对撞机的数据开始，将尖端数据分析技术带到这些设施中。这些进步的一个结果将是，较小学术机构的研究小组将能够更容易地获得必要的计算资源，而这些资源往往只有较大的机构才能获得。消除对这类资源的获取障碍使它们民主化，这是发展多样化劳动力的关键。这项工作还将通过使高能物理数据分析工具与行业计算标准保持一致以及通过培训学生掌握高价值数据科学技能来促进劳动力发展。SCAILFIN项目的主要目标是使用可扩展的网络基础设施(CI)部署人工智能和无可能性推理(LFI)技术和软件，这些CI被开发为集成到现有的CI元素中，如REANA系统。大型强子对撞机数据的分析是该项目的主要科学驱动力，但这项技术足够通用，可以广泛应用。大型强子对撞机实验每年产生数十PB的数据，处理、分析数据并与世界各地的数千名物理学家共享是一个巨大的挑战。为了将观测数据转化为对基础物理的洞察，需要对重要的量子力学过程和探测器对它们的响应进行高水平的详细和准确的模拟。对可扩展CI的投资使科学家能够使用ML方法来克服数据密集型科学中固有的挑战，例如模拟知情推理，这将增加这些实验的发现范围。拟议的可扩展CI组件的开发将催化聚合研究，因为1)抽象的LFI问题公式已经证明自己是各种科学问题的“语言”；2)许多任务的当前工具由于缺乏针对具有计算密集型模拟器的数据密集型问题的可扩展性而受到限制；3)由于设计的原因，项目正在开发的工具被设计为可扩展并可立即部署到不同的计算资源集合上；4)集成其他常用的工作流语言以驱动ML组件的优化和协调大规模工作流将降低来自其他领域的研究人员的进入门槛。该项目由计算机和信息科学与工程总局高级网络基础设施办公室支持。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Mining for Dark Matter Substructure: Inferring Subhalo Population Properties from Strong Lenses with Machine Learning

• DOI: 10.3847/1538-4357/ab4c41
• 发表时间: 2019-09
• 期刊: The Astrophysical Journal
• 作者: J. Brehmer;S. Mishra-Sharma;Joeri Hermans;Gilles Louppe;Kyle Cranmer

Effective LHC measurements with matrix elements and machine learning

利用矩阵元素和机器学习进行有效的大型强子对撞机测量

• DOI: 10.1088/1742-6596/1525/1/012022
• 发表时间: 2020
• 期刊: Journal of Physics: Conference Series
• 作者: Brehmer, J.;Cranmer, K.;Espejo, I.;Kling, F.;Louppe, G.;Pavez, J.
• 通讯作者: Pavez, J.

Benchmarking simplified template cross sections in W H production

• DOI: 10.1007/jhep11(2019)034
• 发表时间: 2019-08
• 期刊: Journal of High Energy Physics
• 影响因子: 5.4
• 作者: J. Brehmer;S. Dawson;S. Homiller;F. Kling;T. Plehn

Mining gold from implicit models to improve likelihood-free inference

• DOI: 10.1073/pnas.1915980117
• 发表时间: 2020-03-10
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Brehmer, Johann;Louppe, Gilles;Cranmer, Kyle
• 通讯作者: Cranmer, Kyle

The frontier of simulation-based inference

• DOI: 10.1073/pnas.1912789117
• 发表时间: 2020-12-01
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Cranmer, Kyle;Brehmer, Johann;Louppe, Gilles
• 通讯作者: Louppe, Gilles