RUI: Machine Learning Approaches for Accelerating Scientific Discovery in Nuclear Physics

核物理实验已经开始产生越来越大的数据量。这种现象是由各种技术进步推动的-例如，仪器的灵敏度提高，使探测器能够以更高的分辨率记录信息，以及粒子加速器的升级，使它们能够利用更高亮度的光束，从而在每次实验中产生更多的反应。因此，传统的分析方法变得难以处理。在理论核物理学中，精确的计算和预测可能在计算上是昂贵的，因此需要允许快速预测和函数近似的替代模型。PI计划使用机器学习（ML）的工具和技术来解决这些挑战，机器学习是统计学，数学和计算机科学的交叉领域，涉及设计，构建和研究计算系统，这些系统可以通过积累经验来改进任务。基于ML的系统擅长从数据中提取模式和进行推断，并且是许多具有挑战性的计算领域（如计算机视觉和机器翻译）的首选方法。这项工作将把尖端的机器学习技术应用于核物理中的长期问题。PI将与本科生合作进行这项研究，他们将接受密切的指导和科学培训。这项工作中开发的算法，统计模型和软件旨在帮助稀有同位素束设施（FRIB），阿贡国家实验室（阿贡），托马斯杰斐逊国家加速器设施（JLab）和即将到来的电子离子对撞机（EIC）的科学发现。PI将使用机器学习技术：（1）改进FRIB实验的数据分析方法，（2）创建由JLab和计划的EIC使用的实验数据提供信息的新理论模型，以及（3）优化FRIB和阿贡的光束传输技术。第一个目标将通过适应和实现卷积神经网络和上下文编码器等神经网络架构来实现，这些架构通常用于监督图像分析应用。第二个目标将通过利用无监督学习技术来实现，包括生成式建模方法，如生成对抗网络和自动编码器，同时利用混合密度网络等预测分布的方法。最后，通过应用深度强化学习领域的最接近策略优化等算法实现第三个目标。该奖项反映了NSF的法定使命，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。

Experiments in nuclear physics have begun producing increasingly large volumes of data. This phenomenon has been driven by various advances in technology - for example, the increased sensitivity of the instrumentation that allows detectors to record information at a higher resolution, and upgrades to particle accelerators that enables them to utilize higher luminosity beams leading to more reactions per experiment. Traditional analysis methods are thus becoming intractable. In theoretical nuclear physics, accurate calculations and predictions can be computationally expensive, and surrogate models that allow for fast predictions and functional approximations are thus desired. The PIs plan to address these challenges using tools and techniques from machine learning (ML), a field at the intersection of statistics, mathematics, and computer science that is concerned with designing, building, and studying computational systems that improve at a task with accrued experience. ML-based systems excel at extracting patterns and drawing inferences from data, and are the method of choice in a number of challenging computational domains, such as computer vision and machine translation. This work will apply cutting-edge ML techniques to standing issues in nuclear physics. The PIs will conduct this research in collaboration with undergraduate students, who will receive close mentorship and scientific training.The algorithms, statistical models, and software developed in this work is designed to aid in scientific discoveries at the Facility for Rare Isotope Beams (FRIB), Argonne National Laboratory (Argonne), Thomas Jefferson National Accelerator Facility (JLab), and the upcoming Electron Ion Collider (EIC). The PIs will use machine learning techniques to: (1) improve data analysis methods for experiments at FRIB, (2) create novel theoretical models informed by experimental data for use at JLab and the planned EIC, and (3) optimize beam delivery techniques at FRIB and Argonne. The first goal will be achieved through the adaptation and implementation of neural network architectures such as Convolutional Neural Networks and Context Encoders, which are commonly used in supervised image analysis applications. The second goal will be approached by utilizing techniques from unsupervised learning, including generative modeling methods such as Generative Adversarial Networks and Autoencoders, while leveraging methods that predict distributions like Mixture Density Networks. Finally, the third goal will be realized by applying algorithms such as Proximal Policy Optimization from the area of deep reinforcement learning.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.