优先级 50 · 多信使触发与联合
NS-UNO: Neutron Star EoS Inference from an Unconstrained Number of Observations
NS-UNO:从无约束数量观测中推断中子星状态方程
Valéria Carvalho (CFisUC, Department of Physics, University of Coimbra, P-3004 - 516 Coimbra, Portugal; Nicolaus Copernicus Astronomical Center, Polish Academy of Sciences, Bartycka 18, 00-716, Warsaw, Poland), Márcio Ferreira (CFisUC, Department of Physics, University of Coimbra, P-3004 - 516 Coimbra, Portugal), Michał Bejger (Nicolaus Copernicus Astronomical Center, Polish Academy of Sciences, Bartycka 18, 00-716, Warsaw, Poland; INFN Sezione di Ferrara, Via Saragat 1, 44122 Ferrara, Italy) et al.
原文摘要Abstract
Future multimessenger observations of neutron stars (NS) are expected to substantially increase both the number and precision of astrophysical constraints on the equation of state (EoS) of dense matter. This motivates inference frameworks capable of accommodating a variable, non fixed number of observations while preserving the posterior information associated with each measurement. In this work, we introduce NS-UNO, a Neural Posterior Estimation framework for NS EoS inference designed to accommodate an Unconstrained Number of Observations (UNO). NS-UNO combines a hierarchical DeepSets model with a conditional normalising flow, enabling a single trained model to perform inference from mass-radius observation sets of varying size, with each observation represented by a set of posterior samples. We demonstrate accurate and well calibrated posterior reconstructions using a model trained jointly on piecewise polytropic and non-parametric Gaussian process EoS ensembles. The reconstruction improves as observations probe a broader range of NS masses, while remaining robust to variations in the number and precision of the observations. The model also generalises to EoSs outside the families used during training. Finally, we qualitatively demonstrate the framework on current multimessenger constraints from NICER and GW170817. NS-UNO provides a flexible and scalable approach to NS EoS inference, naturally suited to the increasingly diverse observational datasets expected from next generation multimessenger astronomy.
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AI 综述
AI-generated · 以原文为准
亮点首个不依赖固定观测数量的 NS EoS NPE 框架,能直接消费逐观测后验样本;在 PT/GP 混合训练下校准良好,并可泛化至未见 EoS 族。
脉络与展望此前 NS EoS 的 NPE 工作 Carvalho+ 2025 虽验证了摊销后验推断,但假设固定观测数量并以点估计输入;同期若干固定维度方法(如 Thakur+ 2026)也难直接处理逐观测后验样本集合。本文引入层次化 DeepSets(Zaheer+ 2018)与条件归一化流(Winkler+ 2019),在观测—样本两级集合上做置换不变聚合,使单一模型适配任意数量观测。混合分段多方与高斯过程 EoS 训练后,模型在直接探测密度区内校准良好,且质量覆盖范围比单纯增加观测数量更关键。未来可扩展至潮汐形变等异构多信使观测量和非高斯后验样本,并在训练中纳入相变类 EoS,以提升对真实 NICER/GW170817 数据的定量推断能力。
预印本 2026-08-31 · 收录 2026-09-01