Collaborative Research: Elements: Enabling Particle and Nuclear Physics Discoveries with Neural Deconvolution

合作研究：元素：通过神经反卷积实现粒子和核物理发现

• 批准号: 2311667
• 负责人: Miguel Arratia
• 金额: $ 23.31万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2311667&HistoricalAwards=false
• 关键词: Collaborative; Research; Elements; Enabling; Particle

Even though large physics experiments are able to detect complex properties of reaction rates, such foundational scientific quantities are always compressed due to limitations in statistical methods and exchange platforms. The current paradigm introduces significant barriers for scientific discovery and data reusability. Comparisons between experiments with different compression schemes is challenging. Furthermore, the compression schemes necessarily throw out potentially useful information, which may be needed to explore interesting phenomena. This information loss likewise limits the long-term utility of archived data, which may be of scientific interest long after the experiment that generated it ends. Recent advancements in machine-learning methods initiated by the PIs and others solve these issues by enabling measurements directly in the un-compressed (or minimally compressed) data. However, there is currently no standard or platform for sharing such data, and therefore, no measurements of this kind with actual data have been published to date. This project builds open source cyberinfrastructure for publishing and reusing un- or minimally compressed measurements for research and educational purposes. These tools are widely applicable across physics domains and data from electron-proton collisions are used to test and benchmark the frameworks. This project serves the national interest, as stated by NSF's mission, by promoting the progress of science. The publication of minimally processed data greatly extends the practical lifetime of experimental facilities, enabling high-quality scientific analyses well beyond the time a detector is running and the researchers who collected the data. Many analyses are simply not possible with existing protocols where only limited numerical results are published alongside academic papers. Minimally compressed data can be studied without computationally expensive and often proprietary detector simulations, and are therefore of great interest for a first exposure to research by early career scientists in training.This project builds upon recent advances by the PIs and others in the development of machine machine learning solutions to measurements of reaction rates in large physics experiments. Cyberinfrastructure is created for publishing and reusing measurements created by these machine learning algorithms. The project develops an exchange format for sharing machine learning-based measurements whose data representation is neural networks, unlike the tabular, often histogram, format of traditional measurements. This format is integrated with a software platform that enables these data to be readily findable, accessible, interoperable, and reusable (FAIR). This cyberinfrastructure is tested with a prototype science pipeline, starting from a first unbinned measurement as input into the platform and ending with an analysis that reinterprets it. In the process, practical software is developed and made available to other researchers to carry out unbinned measurements and to reuse published data. These tools are integrated with widely-used frameworks in particle, nuclear, and astrophysics in order to accelerate their adaptation. Such developments are also used to broaden participation in fundamental physics and applied machine learning for undergraduate researchers, including those who would not normally have access to large experimental and computing resources. This is enabled in part by significantly reduced computational resources required to carry out forefront analysis since computationally expensive experimental tools including detector simulations are not needed.Undergraduate researchers are involved in the development and testing of the newinfrastructure.This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Physics at the Information Frontier program 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.

即使大型物理实验能够检测出反应速率的复杂特性，但由于统计方法和交换平台的限制，这种基础科学量总是被压缩。 当前的范式引入了科学发现和数据可重复使用性的重大障碍。具有不同压缩方案的实验之间的比较具有挑战性。此外，压缩方案必然会抛出潜在的有用信息，这可能是探索有趣现象所需的。这些信息损失同样限制了存档数据的长期效用，这可能是在产生其结束的实验很久以后就具有科学意义的。 PIS和其他人启动的机器学习方法的最新进展通过直接在未压缩（或最小压缩）数据中启用测量来解决这些问题。但是，目前尚无共享此类数据的标准或平台，因此，迄今为止尚未发布此类数据的测量。 该项目建立了开源网络基础架构，用于出版和重复进行研究和教育目的的非临时压缩测量。 这些工具广泛适用于物理域，电子普罗顿碰撞中的数据用于测试和基准测试框架。 正如NSF的使命所述，该项目通过促进科学进步来实现国家利益。最少处理数据的发布大大扩展了实验设施的实际寿命，使高质量的科学分析远远超出了检测器正在运行的时间和收集数据的研究人员。对于仅与学术论文一起发布有限的数值结果的现有协议，几乎不可能进行许多分析。无需计算昂贵且经常专有的探测器模拟，可以研究最小的压缩数据，因此，早期职业科学家在培训中首次接触研究的研究引起了极大的兴趣。该项目基于PIS和其他人在大型物理学实验中测量反应率的机器学习解决方案的最新进步。 Cyber​​inFrastructure是为这些机器学习算法创建的发布和重用测量结果而创建的。该项目开发了一种用于共享基于机器学习的测量的交换格式，其数据表示为神经网络，与表格（通常是直方图）的传统测量格式不同。这种格式与软件平台集成在一起，该软件平台使这些数据很容易找到，可访问，可互操作和可重复使用（公平）。该网络基础结构通过原型科学管道进行了测试，从第一个未键的测量开始，作为对平台的输入，并以对其进行重新解释的分析结束。在此过程中，开发了实用软件并提供给其他研究人员，以进行无上线的测量并重用已发布的数据。 这些工具与粒子，核和天体物理学中的广泛使用的框架集成在一起，以加速其适应性。 此类发展还用于扩大本科研究人员的基本物理和应用机器学习的参与，包括那些通常无法获得大型实验和计算资源的人。 由于不需要计算上的实验工具，包括探测器模拟，包括探测器的研究人员参与了新的基础设施的开发和测试。该奖项由高级网络基础设施办公室授予物理学的物理学奖学金，该奖项是在物理领域的物理领域，该奖项由纽约基础设施奖。 NSF的法定使命，并使用基金会的知识分子优点和更广泛的影响审查标准来评估值得支持。