Canizares Priscilla, Field Scott E, Gair Jonathan, Raymond Vivien, Smith Rory, Tiglio Manuel
Institute of Astronomy, Madingley Road, Cambridge CB3 0HA, United Kingdom.
Department of Applied Mathematics and Theoretical Physics, Wilberforce Road, Cambridge CB3 0WA, United Kingdom.
Phys Rev Lett. 2015 Feb 20;114(7):071104. doi: 10.1103/PhysRevLett.114.071104.
Inferring the astrophysical parameters of coalescing compact binaries is a key science goal of the upcoming advanced LIGO-Virgo gravitational-wave detector network and, more generally, gravitational-wave astronomy. However, current approaches to parameter estimation for these detectors require computationally expensive algorithms. Therefore, there is a pressing need for new, fast, and accurate Bayesian inference techniques. In this Letter, we demonstrate that a reduced order modeling approach enables rapid parameter estimation to be performed. By implementing a reduced order quadrature scheme within the LIGO Algorithm Library, we show that Bayesian inference on the 9-dimensional parameter space of nonspinning binary neutron star inspirals can be sped up by a factor of ∼30 for the early advanced detectors' configurations (with sensitivities down to around 40 Hz) and ∼70 for sensitivities down to around 20 Hz. This speedup will increase to about 150 as the detectors improve their low-frequency limit to 10 Hz, reducing to hours analyses which could otherwise take months to complete. Although these results focus on interferometric gravitational wave detectors, the techniques are broadly applicable to any experiment where fast Bayesian analysis is desirable.
推断合并致密双星的天体物理参数是即将到来的先进LIGO-Virgo引力波探测器网络以及更广泛的引力波天文学的关键科学目标。然而，当前这些探测器的参数估计方法需要计算成本高昂的算法。因此，迫切需要新的、快速且准确的贝叶斯推理技术。在本信函中，我们证明了一种降阶建模方法能够实现快速参数估计。通过在LIGO算法库中实施降阶求积方案，我们表明，对于早期先进探测器配置（灵敏度低至约40赫兹），在非自旋双中子星并合的9维参数空间上进行贝叶斯推理的速度可加快约30倍；对于灵敏度低至约20赫兹的情况，速度可加快约70倍。随着探测器将其低频极限提高到10赫兹，这种加速将增至约150倍，将原本可能需要数月完成的分析缩短至数小时。尽管这些结果聚焦于干涉式引力波探测器，但这些技术广泛适用于任何需要快速贝叶斯分析的实验。
