Nuclear Theory·nucl-th
12 papers—5 primary·7 cross-listed
[Submitted on 13 Feb 2026]
MFGSB, a computer code for self-consistent mean-field calculations of atomic nuclei using the Gaussian expansion method, is now accessible at Chiba University Repository.
[Submitted on 13 Feb 2026]
M.A. Martemianov · B.M. Abramov · S.A. Bulychjov · I.A. Dukhovskoy · V.V. Kulikov · A.A. Kulikovskaya · M.A. Matsyuk
The development of nucleus-nucleus interaction models is a rapidly developing area of heavy-ion physics. Recently, a new model DCM-QGSM-SMM, developed at JINR and oriented toward use within the NICA project at energies of several GeV/nucleon, became available. However, the mechanisms of nuclear interactions used in this model can potentially operate effectively at lower energies. In this paper, the model predictions are compared with the FRAGM and FIRST/GSI experimental data in the energy range of nucleus-nucleus interactions starting from 300 MeV/nucleon, as well as with the predictions of other models used in this energy range.
[Submitted on 14 Feb 2026]
Alessandro Lovato🇺🇸 · Giuseppe Carleo🇨🇭 · Bryce Fore🇺🇸 · Morten Hjorth-Jensen🇳🇴 · Jane Kim🇺🇸 · Arnau Rios🇪🇸 · Noemi Rocco🇺🇸
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.
[Submitted on 15 Feb 2026]
Shuochong Han🇨🇳 · Ang Li🇨🇳
We perform a comprehensive Bayesian analyses of Au + Au collision data at 1.23 GeV/nucleon using an isospin-dependent Boltzmann-Uehling-Uhlenbeck transport model that incorporates a momentum-dependent mean field and medium-modified baryon-baryon cross sections. The model parameters are calibrated to empirical properties of nuclear matter at saturation density, with particular attention to variations in the incompressibility . Within a Bayesian statistical framework and using a Gaussian Process emulator, we simultaneously extract constraints on the incompressibility and the in-medium baryon-baryon scattering modification factor by systematically comparing model predictions with HADES measurements of proton collective flow, including the slopes ( and ) of directed and triangular flow, as well as elliptic () and quadrupole () flow observables. We find that the extracted incompressibility favors relatively small values, indicating a soft nuclear equation of state, while the inferred average values fall at -, suggesting mild suppression of baryon-baryon cross sections in the medium. Furthermore, we demonstrate that transport models employing momentum-independent mean fields require stiffer equations of state and stronger in-medium corrections to reproduce the same observables. These results highlight the critical role of momentum dependence in the mean field and its interplay with in-medium scattering in constraining the properties of dense nuclear matter from heavy-ion collisions.
[Submitted on 15 Feb 2026]
Hiroyuki Tajima🇯🇵 · Kei Iida🇯🇵 · Toru Kojo🇯🇵 · Haozhao Liang🇯🇵
The dense matter equation of state is of great interest due to the recent development of astrophysical observations for neutron stars. A rapid increase in pressure indicates a continuous crossover from a hadron phase to a quark phase without any phase transitions, yet its microscopic mechanism remains elusive. Recently, a peak in the speed of sound and a baryon momentum-shell structure, which are predicted from a quarkyonic matter picture, have been regarded as key features of the hadron-quark crossover. In this work, we explore a field-theoretical framework to describe the hadron-quark crossover, drawing an analogy with the Bose-Einstein condensate to Bardeen-Cooper-Schrieffer (BEC-BCS) crossover established in ultracold atomic experiments. Strikingly, a peak in the speed of sound and the baryon momentum-shell structure can simultaneously be explained by the tripling fluctuation effect arising from a different context of quantum many-body physics. We demonstrate these properties in a simplified model and provide a microscopic derivation of the quarkyonic matter model within our field-theoretical framework.
first authorsco-authorsvia INSPIRE