arXiv subjects

Xiao-Yan Zhu

Publications and source records attributed to Xiao-Yan Zhu.

6 recordsLinked to original sources

arXiv2026

Microscopic Calculation of Electric Quadrupole Effective Charges in Exotic Nuclei

Electric quadrupole ($E2$) effective charges are evaluated based on the self-consistent relativistic Hartree-Fock single-particle states, with core-polarization corrections resummed to all orders using the Tamm-Dancoff approximation (TDA). Configuration-interaction relativistic Hartree-Fock (CI-RHF) calculations employing the TDA effective charges well reproduce the $B(E2)$ strength for neon isotopes from stability to the neutron drip line. We find that polarization charges associated with continuum states are significantly quenched due to their extended density distributions and weak coupling to the core, underscoring the critical role of continuum effects in $E2$ transition evaluations for exotic nuclei. Moreover, the CI-RHF model predicts a suppressed $B(E2; 2^+_2 \to 0^+_2)$ in $^{30}$Ne, together with strong in-band $B(E2)$ strengths of the yrast band, suggesting the coexistence of a nearly spherical excited $0^+_2$ state and a deformed ground state within the $N=20$ "island of inversion".

nucl-th↗

arXiv2026

Discovering the Gell-Mann-Okubo Formula with Kolmogorov-Arnold Networks

Uncovering physical laws from experimental data is a fundamental goal of theoretical physics. In this work, we apply the spline-based, interpretable Kolmogorov-Arnold Network (KAN) to explore the algebraic structure underlying the baryon octet and decuplet mass spectra. Within a symbolic regression framework and without imposing theoretical priors, KAN autonomously recovers the classical Gell-Mann-Okubo mass relations and accurately extracts the associated SU(3) symmetry-breaking parameters. Compared to conventional fitting approaches, this method achieves comparable predictive accuracy while offering substantially improved interpretability and analytic transparency. Our results demonstrate the potential of KAN as a powerful tool for symbolic discovery in hadron physics and for bridging data-driven modeling with fundamental physical laws.

hep-ph↗

arXiv2026

Centrifugal-corrected harmonic oscillator model for spherical proton emitters

In the present work, we propose an improved harmonic oscillator model to systematically evaluate the proton radioactivity half-lives in spherical nuclei, incorporating centrifugal potential effects. By fitting the experimental data, the centrifugal parameter $d = 0.143$ for the correction term $dl(l+1)$ and nuclear potential depth $V_0 = 62.4$ MeV are obtained. The model integrates the relativistic mean field (RMF) theory with the BCS method based on the DD-ME2 force to determine spectroscopic factors $S_p$. Moreover, by verifying the linear relationship between the logarithm of the normalized width $\log_{10}{γ^2}$ and fragmentation potential $V_{frag}$, the connection between nuclear structure and tunneling dynamics is confirmed, and an analytical expression for the adjustable parameter $d$ corresponding to the centrifugal potential is derived as $d^{\rm{Ae}}$ $\approx$ 0.167. Compared with $d^{\rm{Ae}}$, the modified model based on $d$ yields results in better agreement with experimental half-lives, and is able to control the error of the experimental data within a factor of 2.4. Furthermore, the extended improved model is used to predict the half-lives of some possible proton radioactivity candidates in NUBASE2020 that are energetically allowed or have been observed but not yet quantified. This work improves the accuracy of proton radioactivity studies and provides a robust theoretical framework for future nuclear structure research.

nucl-th↗

arXiv2026

Correlation between nuclear isospin asymmetry and $α$-particle preformation probability for superheavy nuclei from a Bayesian inference

In the study of $α$ decay within the superheavy nuclear region ($Z \geq 90$ and $N \geq 140$), the $α$-particle preformation probability $P_α$ serves as a crucial physical quantity linking nuclear structure to decay observables. We introduce a phenomenological model incorporating the decay energy $Q_α$, mass number $A$, orbital angular momentum $l$, isospin asymmetry $I$, and unpaired nucleon effect. For the first time, a Bayesian inference method combined with Markov Chain Monte Carlo (MCMC) sampling has been employed to impose global constraints on the model parameters, enabling the systematic and high-precision calculation of $P_α$. The results reveal a significant suppressing effect of isospin asymmetry on $P_α$, a finding independently corroborated by random forest-based feature importance analysis, which identified $I$ as a dominant factor. Furthermore, calculations using the maximum a posteriori (MAP) parameters not only reproduce the shell effect at $N=152$ but also yield $α$ decay half-life predictions in excellent agreement with experimental ones, thereby validating this model universality. This work provides the first global analysis tool for probing the $α$ preformation mechanism in superheavy nuclei, underscores the potential of the Bayesian framework for inverting complex nuclear physics problems, and establishes a reliable theoretical benchmark for guiding future experimental exploration of superheavy nuclei.

nucl-th↗

arXiv2026

Extracting Transport Properties of Quark-Gluon Plasma from the Heavy-Quark Potential With Neural Networks in a Holographic Model

Using Kolmogorov-Arnold Networks (KANs), we construct a holographic model informed by lattice QCD data. This neural network approach enables the derivation of an analytical solution for the deformation factor $w(r)$ and the determination of a constant $g$ related to the string tension. Within the KANs-based holographic framework, we further analyze heavy quark potentials under finite temperature and chemical potential conditions. Additionally, we calculate the drag force, jet quenching parameter, and diffusion coefficient of heavy quarks in this paper. Our findings demonstrate qualitative consistency with both experimental measurements and established phenomenological model.

hep-ph↗

arXiv2026

Bayesian neural network with autoencoder for model-based description of $α$-particle preformation factor

$α$ decay is an important probe for studying the structure of heavy and superheavy nuclei, in which the $α$-particle preformation ($P_α$) is a key physical quantity for describing decay half-lives. This work develops a hybrid framework that integrates Bayesian neural networks with autoencoder (BNN-Auto), combined with the cosh potential (CPT), to systematically optimize the constraint and prediction of $P_α$. The model employs variational inference for probabilistic modeling of network weights, naturally providing robust uncertainty quantification for predictions, and utilizes an autoencoder to enhance the robustness of feature representation. Based on experimental data from 535 nuclei, the BNN-Auto method achieves relative improvements in the root mean square deviation ($σ_{\rm{RMS}}$) of $P_α$ prediction by $61.14\%$ on the training set and $54.49\%$ on the validation set. Further analysis reveals that the $P_α$ and half-life extracted by the model exhibit pronounced odd-even staggering and shell effects in isotopic chains with $Z=86-90$ and isotones with $N=124-128$ and $N=150-154$. Moreover, we successfully predict the $α$ decay half-lives of nuclei with $Z=120$ and observe a significant increase in the half-life near $N=184$, which verifies the shell effect of the predicted 'stable island'. This study not only provides a high-precision theoretical description for $α$ decay, but also offers a new machine learning perspective for exploring the structure of superheavy nuclei.

nucl-th↗