Research in Low-Energy Nuclear Theory

About This Grant

The strong interaction is responsible for the binding of protons and neutrons into atomic nuclei. Improved quantitative understanding of how this happens is essential not only for fundamental nuclear research at US experimental facilities but for progress in astrophysics, for experiments on the nature of neutrinos, and for applications to energy and homeland security. The era of precision calculations of nuclear structure and reactions is underway, enabled in part by research findings and products from past NSF grants to the PI. New activities use machine learning and quantum computing tools and will include extending the range and capabilities of statistical methods for assessing theoretical uncertainties and for physics discovery, improving the extraction of information from experiment that minimally depends on model assumptions, and developing and testing a novel approach to systematically describing the full nuclear landscape. The training received by undergraduates and graduate students in carrying out these activities contributes directly to the building of a skilled scientific workforce. The mix of analytical and numerical computation the students and postdocs must employ is excellent preparation for both academic and industrial careers that is validated by the strong track record of past members of this group. Activities are being pursued in three categories: statistical methods for effective field theory (EFT) uncertainty quantification, development and application of machinery for process-independent quantities, plus explorations of nuclear renormalization group (RG) for quantum computers, and path integral formulations for finite density nuclear systems and for artificial neural networks (ANNs). Extensions of reduced basis emulators will enable the use of Bayesian methods for uncertainty quantification of nuclear interactions, few- and many-body systems, and electroweak probes. This project will extend the RG perspective, which exploits scale and scheme dependence in nuclear reactions. Projects range from further treatments of short-range-correlation physics relevant for JLab experiments, to the novel treatments of knock-out and other reactions for FRIB and astrophysics, to the implementation of the similarity RG as a quantum computing algorithm. Finally, path-integral-based methods will be employed on two fronts: in the background-field formalism to advance toward the goal of EFT for nuclear DFT and to provide guidance and tools for improving implementations of neural networks for nuclear applications. These projects all contribute to the goal of microscopic, model-independent calculations of nuclei; they will impact forefront problems in low-energy nuclear physics and multi-messenger astrophysics as outlined in the 2023 Long Range Plan for Nuclear Science. This project advances the objectives of "Windows on the Universe: the Era of Multi-Messenger Astrophysics", one of the 10 Big Ideas for Future NSF Investments. 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.