Elements: RAD Discoveries for Fundamental Physics

元素：RAD 基础物理发现

• 批准号: 2209917
• 负责人: Georgia Karagiorgi
• 金额: $ 59.93万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2209917&HistoricalAwards=false
• 关键词: Elements; RAD; Discoveries; Fundamental; Physics

This project supports a diverse team of physicists and computer scientists to leverage recent advancements in AI and newly available particle detector capabilities in order to develop new computationally- and energy-efficient technology that is capable of detecting rare and unpredictable patterns in data with unprecedented intelligence. Such technology finds wide applicability in industry, medicine, national security, but also fundamental physics research. The latter is a key focus of the project, where the developed infrastructure enables new searches for yet-undiscovered physics phenomena using state-of-the-art particle detectors at national and international particle accelerator facilities. These searches represent a major departure from the methods that scientists have been applying for decades in particle physics experiments to discover new phenomena, in that they are no longer confined within an existing and limited understanding of how the Universe works, or a prior expectation for how rare and unpredictable patterns in data may manifest. The developed technology allows the data itself, rather than prior knowledge, to guide new discoveries about how Nature works at its most fundamental level, and aims to revolutionize the way new discoveries are made.This project aims to transform fundamental discoveries in particle and astro-particle physics by developing new Cyberinfrastructure (CI) for real-time anomaly detection (“RAD”). The newly developed CI enables searches that can uncover “unknown” physics through rare, unpredictable phenomena. Traditionally, discoveries of rare processes through particle and astro-particle experiments have relied on scientists’ ability to accurately predict new physics phenomena, and to subsequently selectively look for them within the data by using algorithms trained on predicted unknowns. In a new era of scientific discovery, driven by unprecedented data statistics and Artificial Intelligence (AI) advances, scientists instead may now use AI-powered tools that let the data guide expectation to selectively identify rare and unpredictable signatures that may lie within the data itself, and which may be signatures of new fundamental physics phenomena in nature. The project brings together physicists and computer scientists at Columbia and Princeton, including students, post-doctoral researchers, and CI professionals, and additional collaborators at other US institutions and national labs, to develop anomaly detection algorithms that are executable on resource constrained platforms, commonly employed as on-detector data processing devices at particle physics experiments. Through targeted developments and demonstrations at existing particle detector facilities, the ultimate goal of this project is to develop advanced CI that is energy-efficient, reliable, scalable, and expandable, and thus applicable to trigger systems of current and future generation experiments in the intensity, energy, and cosmic frontiers of particle physics. By design, the delivered CI is capable of fundamental discoveries that are beyond what is envisioned with current trigger frameworks, and has the potential of revolutionizing the way particle and astro-particle physics discoveries are made. The developed CI promises to be broadly applicable to other high-throughput data-selection applications that require real-time, intelligent data processing for the purposes of discovering anomalies in data. These applications can range from neuroscience, to medical imaging, to satellite imaging, cybersecurity, etc.This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Physics at the Information Frontier in the Division of Physics within the Directorate for Mathematical and Physical Sciences.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.

该项目支持一支由物理学家和计算机科学家组成的潜水团队，以利用AI和新近可用的粒子探测器能力的最新进步，以开发新的计算和节能技术，该技术能够在具有前所未有的智能的数据中检测到稀有且不可预测的模式。这样的技术在工业，医学，国家安全以及基本物理学研究中发现了广泛的适用性。后者是该项目的重点，在该项目中，开发的基础设施可以使用国家和国际粒子加速器设施的最先进的粒子探测器进行新的搜索，以对尚未发现的物理现象进行新的搜索。这些搜索与科学家在粒子物理实验中申请了数十年以发现新现象的方法是重大的，因为它们不再局限于对宇宙的现有和有限的理解中，或者对数据在数据中的罕见和不可预测的模式的预期可能不再存在。开发的技术允许数据本身，而不是先验知识，可以指导有关自然在其最基本水平上如何运作的新发现，并旨在彻底改变新发现的方式。该项目旨在通过开发新的Cyber​​inFradstructure（CI）来转化粒子和天体粒子物理学的基本发现（CI），以实时检测（“ RAD”）。新开发的CI可以通过罕见的，不可预测的现象来揭示“揭示”物理的搜索。传统上，通过粒子和星星粒子实验对稀有过程的发现取决于科学家准确预测新物理现象的能力，并通过使用对预测未知数培训的算法在数据中选择性地选择它们。在前所未有的数据统计和人工智能（AI）驱动的科学发现的新时代中，科学家现在可以使用AI驱动的工具来指导数据指导期望有选择性地识别数据本身可能存在的稀有且不可预测的签名，并且可能是本质上新的基本物理学签名的签名。该项目汇集了哥伦比亚和普林斯顿的物理学家和计算机科学家，包括学生，博士后研究人员以及CI专业人员，以及其他美国机构和国家实验室的其他合作者，以开发可在资源约束平台上执行的异常检测算法，这些算法通常用作网络数据处理的粒子处理设备，这些算法是在粒子上使用粒子的设备。通过现有粒子探测器设施的有针对性的开发和演示，该项目的最终目标是开发高级CI，该CI具有节能，可靠，可扩展性和可扩展性，因此适用于触发粒子物理学强度，能量和宇宙边界的当前和未来生成实验的系统。通过设计，交付的CI能够提供基本发现，这些发现超出了当前触发框架所设想的，并且具有革新粒子和天文粒子物理学发现方式的潜力。开发的CI有望广泛适用于需要实时，智能数据处理的其他高通量数据选择应用程序，以发现数据中的异常。这些应用可以从神经科学到医学成像，再到卫星成像，网络安全等。该奖项由高级网络基础设施办公室奖，由物理学在Information Frontier的物理学局共同支持了数学和物理科学局内部物理局的物理领域。该奖项反映了NSF的智力和构建范围的范围。审查标准。