AI 中文总结
研究利用模拟数据探讨高精度中子星半径测量对双中子星可观测性贝叶斯推断的影响，通过多种方法量化信息增益，揭示三种推断模式，表明约0.2千米精度测量可提取大部分信息，还提供通用贝叶斯框架。
AI 中文摘要
双中子星具有相同引力质量但不同半径，是超致密物质中强一阶强子 - 夸克相变最有前景的天体物理特征之一。我们利用典型的1.4 \( M_ \odot \) 中子星的模拟半径数据，研究精度不断提高的中子星半径测量如何改进双中子星可观测性的贝叶斯推断。半径不确定性从当前约0.9千米变化到未来X射线和引力波观测预期的约0.1千米精度。我们使用最大双中子星半径分离\( \Delta R \) 的后验分布、分支可区分性分析模型以及基于分支观测效率和香农熵的互补信息论方法来量化信息增益。联合分析揭示了三种推断模式：\( \sigma_R \gtrsim 0.6 \) 千米时的先验主导模式、\( 0.2 \lesssim \sigma_R \lesssim 0.6 \) 千米时的快速信息增益模式以及\( \sigma_R \lesssim 0.2 \) 千米时的信息饱和模式。这些互补分析一致表明，在当前贝叶斯框架内，约0.2千米精度的半径测量已能提取识别双中子星的大部分可用信息。除了为未来高精度半径测量建立定量观测基准外，这项工作还提供了一个通用的贝叶斯框架，用于量化来自逐渐精确观测的信息增益并确定科学回报递减点。
英文摘要
Twin neutron stars (NSs), characterized by identical gravitational masses but different radii, are among the most promising astrophysical signatures of a strong first-order hadron--quark phase transition in supradense matter. We investigate how increasingly precise NS radius measurements improve the Bayesian inference of twin-star observability using mock radius data for a canonical $1.4\,M_\odot$ NS. Radius uncertainties are varied from the current level of about $0.9$ km to the $\approx 0.1$ km precision anticipated from future X-ray and gravitational-wave observations. We quantify the information gained using the posterior distribution of the maximum twin-star radius separation $ΔR$ together with an analytical model of branch distinguishability and complementary information-theoretic measures based on the branch observational efficiency and the Shannon entropy. The combined analyses reveal three inference regimes: a prior-dominated regime for $σ_R \gtrsim 0.6$ km, a rapid information-gain regime for $0.2 \lesssim σ_R \lesssim 0.6$ km, and an information-saturation regime for $σ_R \lesssim 0.2$ km. These complementary analyses consistently indicate that radius measurements with a precision of about $0.2$ km already extract most of the information available for identifying twin NSs within the present Bayesian framework. Beyond establishing a quantitative observational benchmark for future high-precision radius measurements, this work provides a general Bayesian framework for quantifying the information gain from progressively more precise observations and identifying the point of diminishing scientific returns.
CommentsVersion accepted by Phys. Lett. B
Journal refPhys. Lett. B 880 (2026) 140863
DOI：10.1016/j.physletb.2026.140863
