RUI: Experimental Subatomic Physics at Virginia Union University

该奖项支持弗吉尼亚联合大学(VUU)的一名教授和与他一起工作的学生为两项旨在了解中子基本结构和动力学的高影响测量做出重要贡献。该奖项将通过配备一个对亚原子物理和潜在应用有用的慢控开发设施来加强VUU教师在物理学方面的研究基础设施。通过增加研究机会和接触世界领先的研究设施，这些参与者将获得硬件和软件方面的培训和经验，这些培训和经验对他们未来的STEM职业生涯将是无价的。该奖项由物理部和STEM卓越公平部内的HBCU-UP计划共同支持。这项计划中的研究为杰斐逊实验室的BONuS12和TDIS实验做出了贡献，这些实验使研究近自由中子靶成为可能，并寻求进一步完善中子结构函数。关于中子结构函数的知识目前代表着社区理解上的一个空白，并影响中微子散射测量。建议的工作有助于为TDIS实验开发一种用于监测漂移和目标气体系统的慢速控制系统。在另一项调查中，PI将合作利用人工智能和发布的结构功能数据进行新的F2A计算。事实证明，亚原子物理界对这方面的工作很感兴趣，这种方法将是对目前可用的参数化的补充，因为它利用了经过训练的人工神经网络来计算这个量，而不是根据拟合例程进行内插/外推。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。

This award supports one Virginia Union University (VUU) professor and students working with him in making important contributions to two high-impact measurements aimed at understanding the fundamental structure and dynamics of the neutron. The award will strengthen the research infrastructure available to VUU faculty in physics by outfitting a slow controls development facility that will be useful for subatomic physics and potential applications. Student development will be enhanced by the added availability of research opportunities and exposure to a world-leading research facility that hosts a multi-national group of experienced scientists and engineers, and these participants will gain training and experience in hardware and software that will be invaluable for their future STEM careers. This award is jointly supported by the Division of Physics and the HBCU-UP Program within the Division of Equity for Excellence in STEM. The planned research contributes to the BONuS12 and TDIS experiments at Jefferson Lab, which enable the study of near-free neutron targets and seek to further refine the neutron structure function. Knowledge of the neutron structure function currently represents a gap in the community’s understanding, and affects neutrino scattering measurements. The proposed work contributes to the development of a slow controls system to monitor drift and target gas systems for the TDIS experiment. In a separate line of inquiry, the PI will collaborate on utilizing AI and published structure function data for a new F2A calculation. Work to this end has proven interesting to the subatomic physics community and this method will be complementary to the parameterizations that are presently available, in that it utilizes an artificial neural network that is trained to calculate this quantity, compared to interpolation/extrapolation from fitting routines.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.