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.

该项目支持一个由物理学家和计算机科学家组成的多元化团队，利用人工智能的最新进展和最新可用的粒子探测器功能，开发新的计算和节能技术，能够以前所未有的智能检测数据中罕见和不可预测的模式。这种技术在工业、医学、国家安全以及基础物理研究中都有广泛的应用。后者是该项目的重点，在该项目中，发达的基础设施可以使用国家和国际粒子加速器设施中最先进的粒子探测器对尚未发现的物理现象进行新的搜索。这些搜索代表了科学家们几十年来在粒子物理实验中用于发现新现象的方法的重大背离，因为它们不再局限于对宇宙如何运作的现有和有限的理解，也不再局限于对数据中可能出现的罕见和不可预测的模式的预先预期。这项已开发的技术允许数据本身，而不是先前的知识，来指导关于自然如何在最基本的层面上运作的新发现，并旨在彻底改变新发现的方式。该项目旨在通过开发用于实时异常检测（“RAD”）的新型网络基础设施（CI）来改变粒子和天体粒子物理学的基本发现。新开发的CI可以通过罕见的、不可预测的现象来发现“未知”的物理现象。传统上，通过粒子和天体粒子实验发现的罕见过程依赖于科学家准确预测新物理现象的能力，然后通过使用预测未知的训练算法在数据中有选择地寻找它们。在前所未有的数据统计和人工智能（AI）进步的推动下，科学发现的新时代，科学家现在可能会使用人工智能驱动的工具，让数据引导期望，有选择地识别可能存在于数据本身中的罕见和不可预测的特征，这些特征可能是自然界中新的基本物理现象的特征。该项目汇集了哥伦比亚大学和普林斯顿大学的物理学家和计算机科学家，包括学生、博士后研究人员和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，共同开发可在资源有限的平台上执行的异常检测算法，通常用作粒子物理实验中的探测器数据处理设备。通过在现有的粒子探测器设施上进行有针对性的开发和演示，本项目的最终目标是开发出节能、可靠、可扩展、可扩展的高级CI，从而适用于当前和未来一代实验的触发系统，涉及粒子物理的强度、能量和宇宙前沿。通过设计，交付的CI能够超越当前触发框架所设想的基础发现，并具有彻底改变粒子和天体粒子物理发现方式的潜力。开发的CI有望广泛适用于其他需要实时、智能数据处理以发现数据异常的高吞吐量数据选择应用程序。这些应用范围包括神经科学、医学成像、卫星成像、网络安全等。该奖项由先进网络基础设施办公室颁发，由数学和物理科学理事会物理部信息前沿物理部联合支持。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。