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    Self-consistent neutron stars in a class of massive vector-tensor gravity

    Zhe Luo, Shoulong Li*, and Hongwei Yu†

    • Department of Physics, Key Laboratory of Low Dimensional Quantum Structures and Quantum Control of Ministry of Education, and Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, 410081, China and Hunan Research Center of the Basic Discipline for Quantum Effects and Quantum Technologies, Hunan Normal University, Changsha 410081, China

    • *Contact author: shoulongli@hunnu.edu.cn
    • †Contact author: hwyu@hunnu.edu.cn

    Phys. Rev. D 113, 064041 – Published 23 March, 2026

    DOI: https://doi.org/10.1103/jdx6-v4h3

    Abstract

    Einstein-bumblebee gravity, as a class of massive nonminimally coupled vector-tensor theories, provides a useful framework for constraining Lorentz symmetry breaking through astrophysical observations, largely due to the existence of exact static and spherically symmetric black hole solutions. These solutions are typically obtained under the assumption that the vector-field potential vanishes everywhere once the vector field acquires a nonzero radial vacuum expectation value. However, imposing this assumption globally obstructs the construction of self-consistent compact-star solutions. In this work, we elucidate the origin of this inconsistency through a detailed analysis of the field equations and construct neutron-star configurations by abandoning the global vanishing-potential assumption. Crucially, we show that even without enforcing this condition everywhere, it is violated only in the strong-field interior region and is dynamically restored in the weak-field regime by asymptotic boundary conditions at spatial infinity. As a result, consistency with existing black hole solutions and observational constraints is preserved. Our results establish massive vector-tensor gravity as a unified, natural, and self-consistent framework for compact objects, significantly extending its astrophysical viability beyond black holes and Solar System tests.

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