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Nuclear Theory·nucl-th

Wednesday·August 19, 2026

16 papers—5 primary·11 cross-listed

All16Primary5Cross-lists11
  1. 01

    [Submitted on 18 Aug 2026]

    Generative artificial intelligence for reconstructing neutron-star matter

    Julia Yu. Panteleeva🇩🇪 · Herzallah Alharazin🇩🇪 · Evgeny Epelbaum🇩🇪

    Neutron-star cores hold the only known matter in the universe that is simultaneously cold and strongly interacting, compressed beyond nuclear density into a state of unknown composition. The equation of state links stellar masses, radii and tidal deformabilities to this regime, but recovering this key quantity from sparse observations is an ill-posed inverse problem. Existing analyses bury a prior in a fixed functional form, unevenly weighting admissible solutions and biasing the result. We reconstruct the equation of state with a denoising diffusion model that keeps prior, physics and data separate: it learns an inspectable, physically motivated prior anchored to first-principles nuclear theory, while perturbative-QCD and astrophysical constraints are imposed exactly. Future measurements therefore will update the posterior by reweighting alone, without retraining or resampling. The inferred radius of 12.6 km and tidal deformability of 469 at 1.4 solar masses reproduce Gaussian-process and heavy-ion-informed inferences despite a far broader prior. We find near-conformal but still stiff matter in the heaviest stars, consistent with a gradual hadron-quark crossover and disfavouring a strong first-order phase transition. More broadly, coupling a learned prior to exactly enforced physics establishes a template for ill-posed inverse problems where theory and data constrain different regions.

    Subjects:
    Nuclear Theory (nucl-th); High Energy Astrophysical Phenomena (astro-ph.HE); Instrumentation and Methods for Astrophysics (astro-ph.IM); High Energy Physics — Phenomenology (hep-ph); Nuclear Experiment (nucl-ex)
    arXiv:
    2608.17457 [pdf]
    0 citations
  2. 02

    [Submitted on 18 Aug 2026]

    Data-Driven Statistical Ensembles of Chiral Nuclear Interactions

    Pengsheng Wen · Jeremy W. Holt

    Recent advances in ab initio nuclear theory, machine learning, and Bayesian inference, coupled with increasingly precise nuclear experiments and astrophysical observations, have enabled more robust constraints on fundamental descriptions of the nuclear interaction. Although well-established nonlinear regression methods can identify best-fit sets of low-energy constants at fixed resolution scale, they provide limited insight into the full underlying probability distributions of those constants. A central remaining challenge in nuclear theory is therefore to characterize full probability distributions of nuclear forces across resolution scales. In this work, we employ normalizing flows, a class of expressive generative machine learning models, to infer the joint probability distribution of two-body low-energy constants (LECs) in chiral effective field theory over a wide range of resolution scales. The resulting LEC distributions are shown to accurately reproduce experimental neutron-proton scattering phase-shift distributions. Furthermore, strong non-Gaussian correlations among LECs are revealed, indicating a nontrivial interplay among distinct short-range nuclear dynamics. This work establishes a general framework for constructing statistical ensembles of nuclear interactions that can be systematically constrained by future nuclear experiments and astrophysical observations.

    Comments:
    5 figures
    Subjects:
    Nuclear Theory (nucl-th)
    arXiv:
    2608.17813 [pdf]
    0 citations
  3. 03

    [Submitted on 18 Aug 2026]

    Symbolic Regression for Interpretable Emulation of Proton Collective Flow in Intermediate-Energy Heavy-Ion Collisions

    Nicholas Cox🇺🇸 · Xavier Grundler🇺🇸 · Bao-An Li🇺🇸

    Symbolic regression provides an interpretable machine-learning approach for constructing explicit analytic relations between physical inputs and observables. In this work, we develop symbolic-regression emulators for the isospin-dependent Boltzmann-Uehling-Uhlenbeck (IBUU) transport model and compare their performance with deep neural network (DNN) emulators. Using the same transport-model data employed in our previous emulator studies, we show that symbolic regression can reproduce the proton mid-rapidity slope of transverse flow and elliptic flow with accuracy comparable to that of DNNs, while providing explicit analytic expressions and substantially faster prediction once trained. We further demonstrate the use of symbolic regression in the reverse direction by constructing analytic relations that predict the in-medium nucleon-nucleon cross-section modification factor from the flow observables. Although the symbolic-regression models require substantially longer training times and exhibit greater run-to-run variation than DNNs, their analytic form and rapid evaluation make them promising tools for future transport-model sensitivity and uncertainty analyses.

    Comments:
    12 pages including 4 figures
    Subjects:
    Nuclear Theory (nucl-th); Nuclear Experiment (nucl-ex)
    arXiv:
    2608.17828 [pdf]
    0 citations
  4. 04

    [Submitted on 18 Aug 2026]

    The elliptic wind on jet wakes in high-energy heavy-ion collisions

    Kai-Yi Wu🇨🇳 · Zhong Yang🇺🇸 · Xin-Nian Wang🇨🇳

    Energy loss by fast partons induces a Mach-cone-like medium response as they propagate inside the hot quark-gluon plasma (QGP) in high-energy heavy-ion collisions. Because the QGP is nonuniform and its initial gradients generate collective flow, jet-induced medium response in this evolving system is also distorted by the flow and density gradient. This distortion leads to a broadened jet wake whose transverse width depends on the azimuthal angle of the jet propagation due to the elliptic anisotropy of the density gradient and the flow velocity in noncentral heavy-ion collisions. We propose and calculate the difference between the azimuth-dependent jet-hadron correlations for soft charged hadrons in in-plane and out-plane -jets as a measure of the elliptic broadening of the wake front and the deepening of the diffusion wake. We also study the sensitivity of this observable to the shear viscosity of the QGP. Experimental measurements of the azimuthal modulation of jet wakes induced by the wind of the elliptic flow at RHIC and LHC can provide additional constraints on the transport properties of the QGP.

    Comments:
    5 pages in RevTex with 6 figures and supplemental materials. This version contains corrections of minor typos
    Subjects:
    Nuclear Theory (nucl-th)
    arXiv:
    2608.17967 [pdf]
    0 citations
  5. 05

    [Submitted on 18 Aug 2026]

    Confining density functional approach to the QCD phase diagram at low temperatures and thermal twin stars

    David Blaschke🇵🇱 · Oleksii Ivanytskyi🇩🇪

    We present a density functional-based equation of state for warm, dense nuclear matter with a transition to deconfined quark matter for applications to simulations of supernova explosions and neutron star mergers, but also for the cosmological evolution of Q-balls. For the quark matter equation of state, we employ a recently developed confining density functional approach while nuclear matter is described within a relativistic density functional model of the DD2 class. The phase transition is obtained by a Maxwell construction at constant entropy per baryon. We discuss the solutions of TOV equations for isentropic hybrid stars for the hybrid equation of state model DDf-SFM (DD2-CDF) without (with) color superconductivity and find that at finite temperatures above a critical value of entropy per baryon sequences of disconnected third family branches ("thermal twin stars") may appear for the DDf-SFM model, while they are absent for the color superconducting model and at . We discuss the relation of this critical entropy per baryon to the Seidov criterion of gravitational instability for and find that it is a good guide. We suggest that the presence of thermal twin stars may be regarded as an indicator for the core-collapse supernova explodability of massive blue supergiant stars and thus serve as a new criterion for the reliability of hybrid equation of state models. By this argument, strong color superconductivity shall be excluded and it remains to be shown whether models with moderate diquark pairing could fulfill the thermal twin constraint. For the case of symmetric matter, we compare the resulting hybrid EOS with the flow constraint by Danielewicz et al. and find a a sensitivity of the onset density for deconfinement on the presence or absence of color superconductivity.

    Comments:
    17 pages, 11 figures, 2 tables
    Subjects:
    Nuclear Theory (nucl-th); Solar and Stellar Astrophysics (astro-ph.SR); High Energy Physics — Phenomenology (hep-ph)
    arXiv:
    2608.18038 [pdf]
    0 citations

Affiliations

first authorsco-authorsvia INSPIRE