NS-UNO：基于无约束观测数量的中子星物态方程推断
NS-UNO: Neutron Star EoS Inference from an Unconstrained Number of Observations
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中文总结 AI 辅助
本研究提出NS-UNO框架，结合分层DeepSets模型与条件归一化流，可适应不同数量和精度的中子星多信使观测，实现灵活可扩展的物态方程推断，且具备鲁棒性与泛化性。
中文摘要 AI 辅助
未来对中子星（NS）的多信使观测有望大幅增加致密物质物态方程（EoS）的天体物理约束数量与精度，这促使研究人员开发能够适应可变非固定观测数量、同时保留每次测量相关后验信息的推断框架。本研究提出NS-UNO，一种用于NS EoS推断的神经后验估计框架，旨在适应无约束数量的观测（UNO）。NS-UNO将分层DeepSets模型与条件归一化流相结合，使单个训练好的模型可对不同规模的质量-半径观测集进行推断，每个观测由一组后验样本表示。我们使用在分段多方和非参数高斯过程EoS集成上联合训练的模型，展示了准确且校准良好的后验重建结果：当观测探测更广泛的NS质量范围时，重建效果提升；同时对观测数量和精度的变化保持鲁棒性，还能泛化到训练所用族之外的EoS。最后，我们对当前来自NICER和GW170817的多信使约束定性演示了该框架，NS-UNO为NS EoS推断提供了灵活可扩展的方法，天然适配下一代多信使天文学预期的日益多样化的观测数据集。
英文摘要
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.
发表机构
- University of Coimbra（科英布拉大学）
- Nicolaus Copernicus Astronomical Center, Polish Academy of Sciences（波兰科学院尼古拉·哥白尼天文学中心）
- INFN Sezione di Ferrara（费拉拉意大利国家核物理研究所）
机构由 AI 辅助整理，请以论文原文为准。
