论文标题
使用标准化流量来检测中子星质量 - 拉迪乌斯测量中的密集 - 晶体过渡特征作为数据异常
Detecting dense-matter phase transition signatures in neutron star mass-radius measurements as data anomalies using normalising flows
论文作者
论文摘要
对中子星的观察可用于研究极密集的物质的各个方面，特别是将相变向异国情调状态的可能性，例如去限制的夸克。 我们提出了一种新的数据分析方法，用于检测质量 - 拉迪乌斯测量集中密集的相变的特征，并研究其对观察误差大小和观察次数的敏感性。该方法基于机器学习异常检测，再加上标准化流技术：对没有相变的天体物理观察样本训练的算法，这些算法没有相变签名将相变样品解释为“异常”。为了进行这项研究，我们专注于状态的致密进程，从而导致质量 - 拉迪乌斯序列的分离分支（强相变），使用天体化的中子星级质量功能，以及观察性误差和样本量的各种大小。 该方法显示出可靠地检测到质量 - 拉迪乌斯与相变特征的案例，同时通过减少测量误差和观察次数的增加来提高其灵敏度。当相变质量位于质量函数范围的边缘附近时，我们讨论边缘情况。该方法对电磁和重力波观测的实际测量结果进行了当前的最新选择，该方法给出了不确定的结果，我们将其解释为较小的可用样本量，较大的观察误差和复杂的系统学。
Observations of neutron stars may be used to study aspects of extremely dense matter, specifically a possibility of phase transitions to exotic states, such as de-confined quarks. We present a novel data analysis method for detecting signatures of dense-matter phase transitions in sets of mass-radius measurements, and study its sensitivity with respect to the size of observational errors and the number of observations. The method is based on machine learning anomaly detection coupled with normalizing flows technique: the algorithm trained on samples of astrophysical observations featuring no phase transition signatures interprets a phase transition sample as an ''anomaly''. For the sake of this study, we focus on dense-matter equations of state leading to detached branches of mass-radius sequences (strong phase transitions), use an astrophysically-informed neutron-star mass function, and various magnitudes of observational errors and sample sizes. The method is shown to reliably detect cases of mass-radius relations with phase transition signatures, while increasing its sensitivity with decreasing measurement errors and increasing number of observations. We discuss marginal cases, when the phase transition mass is located near the edges of the mass function range. Evaluated on the current state-of-art selection of real measurements of electromagnetic and gravitational-wave observations, the method gives inconclusive results, which we interpret as due to small available sample size, large observational errors and complex systematics.
