利用归一化流检测第三族致密星
Detecting the third family of compact stars with normalizing flows
AI总结：
本文探讨基于 Normalizing Flows 的异常检测框架，通过中子星质量与半径观测数据检测一阶致密物质相变，发现当半径差 $ΔR > 0.4$ km 时，可在 15 颗星样本中以 95% 准确率检出双分支解。
摘要：
我们探讨了基于 Normalizing Flows (NF) 模型的异常检测框架，该模型在 \cite{PhysRevC.106.065802} 中被引入，用于在中子星（NS）的质量和半径观测中检测是否存在大（破坏稳定性的）致密物质相变，并将检测的可行性与底层质量-半径序列的参数相关联，该序列是致密物质状态方程的泛函。在具有连续 $M(R)$ 解（即无相变）的模拟数据上训练后，NF 被用于确定在给定的一组具有不连续性的 $M(R)$ 观测中发生一阶相变的似然性，即执行异常检测。不同的模拟测试集在 $M(R)$ 图中具有双分支解，由发生相变时的 NS 质量 $M_c$ 和最重强子星与最轻混合星之间的半径差 $ΔR$ 参数化。我们分析了这些参数对 NF 检测一阶相变存在性性能的影响。在结果中，我们报告称，给定一组 15 颗半径不确定度为 $0.2$ km 的恒星，如果 $ΔR > 0.4$ km，则可以以 95% 的准确率检测到双分支解。
英文摘要：
We explore the anomaly detection framework based on Normalizing Flows (NF) models introduced in \cite{PhysRevC.106.065802} to detect the presence of a large (destabilising) dense matter phase transition in neutron star (NS) observations of masses and radii, and relate the feasibility of detection with parameters of the underlying mass-radius sequence, which is a functional of the dense matter equation of state. Once trained on simulated data featuring continuous $M(R)$ solutions (i.e., no phase transitions), NF is used to determine the likelihood of a first-order phase transition in a given set of $M(R)$ observations featuring a discontinuity, i.e., perform the anomaly detection. Different mock test sets, featuring two branch solutions in the $M(R)$ diagram, were parameterized by the NS mass at which the phase transition occurs, $M_c$, and the radius difference between the heaviest hadronic star and lightest hybrid star, $ΔR$. We analyze the impact of these parameters on the NF performance in detecting the presence of a first-order phase transition. Among the results, we report that given a set of 15 stars with radius uncertainty of $0.2$ km, a detection of a two-branch solution is possible with 95\% accuracy if $ΔR > 0.4$ km.
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