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arXiv · 2607.13930

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

Yao Ma·Yong-Liang Ma·Jia-Ying Xiong
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Abstract

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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Yao Ma, Yong-Liang Ma, Jia-Ying Xiong. 2026-07-15. NNStar: An end-to-end AI agent for nuclear matter and neutron star physics. https://arxiv.org/abs/2607.13930

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