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Is the nucleus a many body problem

H. Kuemmel, K. Bleuler

Published Jan 1, 1975• 1 citation
OpenAlex

Related papers

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Experimental_Protocol_Quantum_Tomography_of_Atomic_Nuclei_with_Combined_Spin_Energy_Mode_and_Informational_Trigger

Walter Ayala

The Problem of Nuclear StructureThe atomic nucleus is a quantum many-body system. Conventional probes (photons,electrons, hadrons) interact electromagnetically or strongly with the nucleus, and nonedirectly measures the mass distribution.2.2 The Problem of Nuclear MassMnucleus = Mquarks + Mkinetic + Mgluons + MQCD vacuum + MEM (2)where Mquarks ∼ 1%, Mkinetic ∼ 30%, Mgluons ∼ 30%.2.3 Proposal: Informational Trigger with Combined ModeAn NV center in combined mode (spin + energy) emitsan informational messagethat travels through the substrate channel and acts as aquantum trigger on the atomic nucleus, activating itsinternal degrees of freedom without the need forenergetic excitation.

•Jun 7, 2026•0 citations

Neural-network quantum states for the nuclear many-body problem

Alessandro Lovato, Giuseppe Carleo, Bryce Fore +4 more

A long-standing goal of nuclear theory is to explain how the structure and dynamics of atomic nuclei and neutron-star matter emerge from the underlying interactions among protons and neutrons. Achieving this goal requires solving the nuclear quantum many-body problem with high accuracy across a wide range of length scales and density regimes. In this review, we discuss how artificial neural network representations of the nuclear many-body wave function have significantly extended the capabilities of continuum quantum Monte Carlo methods. In particular, neural network quantum states enable calculations of larger systems than were previously accessible and provide a flexible framework for capturing phenomena that challenge conventional approaches, including the emergence of nuclear clusters and superfluid phases in dense matter. We highlight recent applications to finite nuclei, infinite nuclear and neutron matter, and dynamical processes relevant to lepton-nucleus and nucleus-nucleus scattering. We also discuss conceptual and methodological connections with condensed matter physics, emphasizing developments in neural network quantum states that bridge strongly correlated systems across disciplines. Together, these developments demonstrate how neural-network methods open new avenues toward unified and accurate descriptions of nuclear structure, matter, and reactions.

nucl-thCross-listedarXiv:2602.13826•Feb 17, 2026•0 citations

THE NUCLEAR MANY–BODY PROBLEM

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Effective Interaction in Nuclear Many-Body Problem

Masaru Yasuno

General treatment of nuclear many-body problem will be given in terms of Green functions and the correlation property of nucleus will be exhibited by correlation factor. Effective interaction certifying the independent particle motion is introduced and proved to identify with Brueckner's K-matrix. And on the basis of this point, applying the method of the particle-hole pair to the system, a collective mode will be derived and the effective interaction contributing to collective motion will be studied. It will also be shown that this effective interaction has the property very different from electron plasma case.

Progress of Theoretical Physics•Mar 1, 1961•1 citation
The Problem of Nuclear StructureThe atomic nucleus is a quantum many-body system. Conventional probes (photons,electrons, hadrons) interact electromagnetically or strongly with the nucleus, and nonedirectly measures the mass distribution.2.2 The Problem of Nuclear MassMnucleus = Mquarks + Mkinetic + Mgluons + MQCD vacuum + MEM (2)where Mquarks ∼ 1%, Mkinetic ∼ 30%, Mgluons ∼ 30%.2.3 Proposal: Informational Trigger with Combined ModeAn NV center in combined mode (spin + energy) emitsan informational messagethat travels through the substrate channel and acts as aquantum trigger on the atomic nucleus, activating itsinternal degrees of freedom without the need forenergetic excitation.
A long-standing goal of nuclear theory is to explain how the structure and dynamics of atomic nuclei and neutron-star matter emerge from the underlying interactions among protons and neutrons. Achieving this goal requires solving the nuclear quantum many-body problem with high accuracy across a wide range of length scales and density regimes. In this review, we discuss how artificial neural network representations of the nuclear many-body wave function have significantly extended the capabilities of continuum quantum Monte Carlo methods. In particular, neural network quantum states enable calculations of larger systems than were previously accessible and provide a flexible framework for capturing phenomena that challenge conventional approaches, including the emergence of nuclear clusters and superfluid phases in dense matter. We highlight recent applications to finite nuclei, infinite nuclear and neutron matter, and dynamical processes relevant to lepton-nucleus and nucleus-nucleus scattering. We also discuss conceptual and methodological connections with condensed matter physics, emphasizing developments in neural network quantum states that bridge strongly correlated systems across disciplines. Together, these developments demonstrate how neural-network methods open new avenues toward unified and accurate descriptions of nuclear structure, matter, and reactions.
General treatment of nuclear many-body problem will be given in terms of Green functions and the correlation property of nucleus will be exhibited by correlation factor. Effective interaction certifying the independent particle motion is introduced and proved to identify with Brueckner's K-matrix. And on the basis of this point, applying the method of the particle-hole pair to the system, a collective mode will be derived and the effective interaction contributing to collective motion will be studied. It will also be shown that this effective interaction has the property very different from electron plasma case.