狄利克雷过程高斯混合模型：应用引力波观测定域合并中子星
Dirichlet Process Gaussian-mixture model: An application to localizing coalescing binary neutron stars with gravitational-wave observations
摘要：
我们重建后验分布的位置（天空面积和距离）的一组模拟的二元中子星引力波信号观测先进LIGO和先进处女座。我们使用狄利克雷过程高斯混合模型，一个完全贝叶斯非参数方法，可用于估计概率密度函数与一组灵活的假设。可靠地重建源位置的能力对于多信使天文学是重要的，正如最近的GW 170817所证明的那样。我们表明，探测器网络的早期操作的先进LIGO和先进的处女座，典型的本地化卷$\sim10^4$- $10^5~\mathrm{Mpc^3}$对应于$\sim10^2$ - $10^3$潜在的宿主星系。定位体积是网络信噪比的强函数，大致按$\propto \varrho_{net}^{-6}$缩放。分数局部化随着向网络添加更多检测器而改善。我们的狄利克雷过程高斯混合模型可用于定位在未来的引力波观测运行中检测到的事件，并用于促进及时的多信使后续行动。
英文摘要：
We reconstruct posterior distributions for the position (sky area and distance) of a simulated set of binary neutron-star gravitational-waves signals observed with Advanced LIGO and Advanced Virgo. We use a Dirichlet Process Gaussian-mixture model, a fully Bayesian non-parametric method that can be used to estimate probability density functions with a flexible set of assumptions. The ability to reliably reconstruct the source position is important for multimessenger astronomy, as recently demonstrated with GW170817. We show that for detector networks comparable to the early operation of Advanced LIGO and Advanced Virgo, typical localization volumes are $\sim10^4$--$10^5~\mathrm{Mpc^3}$ corresponding to $\sim10^2$--$10^3$ potential host galaxies. The localization volume is a strong function of the network signal-to-noise ratio, scaling roughly $\propto \varrho_{net}^{-6}$. Fractional localizations improve with the addition of further detectors to the network. Our Dirichlet Process Gaussian-mixture model can be adopted for localizing events detected during future gravitational-wave observing runs, and used to facilitate prompt multimessenger follow-up.
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