Quantum Many-Body Theories and Methods for Nuclear Physics

Michigan State University, East Lansing MI

Investigators

Abstract

Nuclear Physics plays a key role in our quest to understand the Universe, addressing fundamental questions like: 1) How did visible matter come into being and how does it evolve? 2) How does subatomic matter organize itself and what phenomena emerge? 3) Are the fundamental interactions that are basic to the structure of matter fully understood?, and 4) How can the knowledge and technological progress provided by nuclear physics best be used to benefit society? In recent years, researchers have made remarkable progress in our fundamental understanding of the complex and fascinating system that is the nucleus. This progress has been driven by new theoretical insights, increased computational power, and experimental access to new exotic isotopes with a large excess of neutrons or protons. As the frontiers of experimental nuclear physics increasingly move towards the study of exotic nuclei, there is an urgent need for theorists to develop controlled, ab-initio (i.e., “first principles” or microscopic) many-body theories of the nucleus. This is because time honored phenomenological models of nuclear theory, such as the nuclear shell model and mean-field/density functional theory, are “data driven” approaches that require the presence of nearby data to fix parameters. The exploration of previously uncharted regions of the nuclear landscape therefore poses severe challenges for such models. To address these challenges, the present NSF award will work to develop new ab-initio or microscopic many-body theories and methods that are capable of reaching medium-mass nuclei and beyond. Such approaches start directly from the fundamental forces between nucleons and attempt to solve the quantum many-body equations of motion without introducing additional parameters that need to be fit to the nuclei of interest. This is computationally much more challenging than the phenomenological approaches, and requires significant computational and conceptual advances to cover the same regions of nuclei. Under the present award, the team will develop a nuclear many-body modeling infrastructure based on state-of-the-art Coupled-Cluster (CC) Theory and In-Medium Similarity Renormalization Group (IMSRG) methods, leveraged to exploit powerful techniques and algorithms from the fields of Machine Learning (ML) and Quantum Computing. The goal is to develop a comprehensive framework that is capable of providing controlled and predictive calculations for static and dynamic properties over a wide range of conditions, from nuclei to dense matter and neutron stars. These activities will be closely linked with ongoing experimental programs in low-energy nuclear physics nationally and worldwide. 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.