Towards scalable bound-to-resonance extrapolations for few- and many-body systems

Abstract

In open quantum many-body systems, the theoretical description of resonant states of many particles strongly coupled to the continuum can be challenging. Such states are commonplace in, for example, exotic nuclei and hadrons, and can reveal important information about the underlying forces at play in these systems. In this work, we demonstrate that the complex-augmented eigenvector continuation (CA-EC) method, originally formulated for the two-body problem with uniform complex scaling, can reliably perform bound-to-resonance extrapolations for genuine three-body resonances having no bound subsystems. We first establish that three-body bound-to-resonance extrapolations are possible by benchmarking different few-body approaches, and we provide arguments to explain how the extrapolation works in the many-body case. We furthermore pave the way towards scalable resonance extrapolations in many-body systems by showing that the CA-EC method also works in the Berggren basis, studying a realistic application using the Gamow shell model.

11 pages, 7 figures, matched published version

References in corpus (5)

• Shell Model in the Complex Energy Plane
• Ab-initio no-core Gamow shell model calculations of multi-neutron systems
• Non-resonant Density of States Enhancement at Low Energies for Three or Four Neutrons
• Determination of spin-parity quantum numbers of X(2370) as $0^{-+}$ from $J/\psi \to \gamma K_S^0{K}_S^0{\eta }^{\prime }$
• A Complex Scaling Method for Efficient and Accurate Scattering Emulation in Nuclear Reactions

Cited by in corpus (4)

• Non-Hermitian quantum mechanics approach for extracting and emulating continuum physics based on bound-state-like calculations: Detailed description
• Complex-energy eigenvector continuation for nuclear many-body broad resonances
• Complex-scaled no-core shell model calculations of bound and unbound nuclear states in light nuclei
• Efficient emulation of nuclear ground states with neural-network variational Monte Carlo and eigenvector continuation