arXiv subjects

Jia-Ying Xiong

Publications and source records attributed to Jia-Ying Xiong.

6 recordsLinked to original sources

arXiv2026

Hadronic description of nuclear matter and neutron star properties

The composition of the neutron star is one of the most fundamental and long-standing problems in nuclear- and astro-physics. The known properties of nuclear matter, together with the astronomical observations, impose the stringent and interconnected constraints on the theoretical descriptions. In this work, by using the most general quantum hadrodynamics model including $σ, ω, ρ$ and $a_0$ in addition to nucleons, and performing a Bayesian joint analysis of experimental nuclear matter data, the heavy-ion flow pressure, and astrophysical observations including the mass-radius inferences of PSR J0740+6620, PSR J0437$-$4715, PSR J0614$-$3329 and HESS J1731$-$347 and the tidal posterior of GW170817, we point out that the nuclear matter made of only hadrons can provide a unified description of nuclear matter properties and astrophysical observations.In addition, we find that the speed of sound in the GQHD develops a non-monotonic, peak-like structure which is absent in the Walecka-type models TM1, NL3 and FSU-$\delta6.7$. This soft-to-stiff transition, accompanied by a pronounced softening of the symmetry energy, results in small size intermediate mass neutron stars, $R_{1.4}\simeq (11.1-11.4)$~km, together with the maximum mass $(2.2-2.3)M_\odot$, and, to our knowledge, has not been found before in the Walecka-type relativistic mean-field models. What we find here indicate that the sequential measurement of neutron star mass and radius by the next generation facilities, especially that of the intermediate mass neutron stars, is crucial for distinguishing the pure nucleonic stars from the hybrid ones.

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arXiv2026

Thermodynamic signatures do not uniquely identify deconfinement in neutron stars

The smallness of polytropic index $Γ_\varepsilon \lesssim 1.75$ and near conformality of sound velocity $c_s^2 \approx 1/3$ in neutron star matter are usually referred to as signals of the emergence of quark matter. We construct a density-resolved nucleonic reference domain in the $(P/P_{\rm free},Γ_\varepsilon)$ plane using nucleonic EoSs jointly constrained by nuclear matter properties and neutron star mass, radius and tidal data, and found that the domain extends unambiguously below $Γ_\varepsilon=1.75$. Crucially, the nucleonic domain becomes stable against the truncation order only after $Z_0$ and $Z_{sym}$ are included, showing that the higher-order density dependence controls the extrapolation from finite nuclei to neutron-star matter. These findings therefore provide concrete targets for finite-nucleus and heavy-ion experiments, while linking terrestrial nuclear physics directly to multimessenger observations. We compare the domain with smooth equations of state generated by neural networks without phase labels and represented by symbolic regression. Among these reconstructions, $35.29\%$ have complete trajectories inside the nucleonic domain over $0.5\leq n/n_0\leq8$. Within the EoS sample obtained after minimizing the multimessenger loss, the smallest value of $\max_n c_s^2(n)$ is 0.38, above the conformal value $1/3$. In conclusion, we convert microscopic interpretation into a falsifiable, density-resolved null-hypothesis test and show quantitatively that current observations do not reject the nucleonic null over much of the admissible space of equation of state.

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arXiv2026

NNStar: An end-to-end AI agent for nuclear matter and neutron star physics

Constraining the equation of state of dense matter requires confronting effective models with massive data that spans many orders of magnitude in scale, from sub-saturation nuclear matter properties to the masses, radii, and tidal deformabilities of neutron stars. Exploring the high-dimensional coupling space of such a model and fine tuning it against all of these constraints is a labor- and time-intensive task. We present \textsc{NNStar}, an end-to-end artificial-intelligence agent that automates this workflow. Rather than a bespoke application, \textsc{NNStar} is delivered as a portable \emph{skill} for an open large-language-model (LLM) agent platform -- a self-describing module that pairs worked usage conventions with symbolic and numerical physics engines that (i) build a relativistic mean-field model directly from a Lagrangian, (ii) solve the mean-field equations of motion and evaluate the saturation properties, (iii) construct the $β$-equilibrium equation of state, splice it to a crust, and integrate the Tolman--Oppenheimer--Volkoff equations, and (iv) score the resulting predictions through a Bayesian joint analysis against nuclear matter and astrophysical observations. The agent can read a model, fit its parameters, and report the full set of nuclear matter and neutron star observables without human intervention. \textsc{NNStar} therefore provides a new, AI-driven framework for analyzing nuclear matter and neutron-star observations.

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arXiv2026

Neutron star structure and nuclear matter properties from a general Walecka-type model with Bayesian analysis

We establish a Bayesian analysis framework with a general Walecka-type relativistic mean-field model to study dense nuclear matter under constraints from nuclear matter properties and neutron star observations. With experimental and observational data well described, we find that pure hadronic descriptions can generate a peak structure in sound velocity by $ω$, $ρ$, $σ$, and $a_0$ meson mixing, which is crucial for describing both medium and massive neutron stars. As the peak structure is frequently interpreted as a signature of phase transitions, our findings provide a new perspective on the microscopic origin of the sound velocity peak just with pure hadronic matter.

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arXiv2026

Chiral-scale effective field theory for dense and thermal systems

We established a new power counting scheme, chiral-scale density counting (CSDC) rules, for the application of the chiral-scale effective field theory to nuclear matter at finite densities and temperatures. Within this framework, the free fermion gas is at the leading order, while one-boson-exchange interactions appear at the next-to-leading order, and the multi-meson couplings are at higher orders. Then, we applied the CSDC rules to study the nuclear matter properties, and estimated the valid regions of the CSDC rules. It was found that the zero temperature symmetric nuclear matter properties around saturation density and the critical temperature of liquid-gas phase transition can be captured by an appropriate choice of CSDC orders, and the results beyond these regions are align with the chiral nuclear force. Moreover, the evolution of scale symmetry was found to be consistent with previous studies. The results of this work indicate that the quantum corrections may be crucial in the studies of nuclear matter in a wide density region.

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arXiv2024

Insights into neutron star equation of state by machine learning

Due to its powerful capability and high efficiency in big data analysis, machine learning has been applied in various fields. We construct a neural network platform to constrain the behaviors of the equation of state of nuclear matter with respect to the properties of nuclear matter at saturation density and the properties of neutron stars. It is found that the neural network is able to give reasonable predictions of parameter space and provide new hints into the constraints of hadron interactions. As a specific example, we take the relativistic mean field approximation in a widely accepted Walecka-type model to illustrate the feasibility and efficiency of the platform. The results show that the neural network can indeed estimate the parameters of the model at a certain precision such that both the properties of nuclear matter around saturation density and global properties of neutron stars can be saturated. The optimization of the present modularly designed neural network and extension to other effective models are straightforward.

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