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arXiv 1404.6284gr-qc
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降阶模型加速引力波参数估计

Accelerated gravitational-wave parameter estimation with reduced order modeling

Priscilla Canizares, Scott E. Field, Jonathan Gair, Vivien Raymond, Rory Smith, Manuel Tiglio

摘要:

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引力波探测器网络的一个关键科学目标,更普遍的是,引力波天文学。然而,目前的参数估计方法,这种情况下,可能会导致计算上棘手的问题,在实践中。因此,迫切需要新的、快速和准确的贝叶斯推断技术。在这封信中,我们证明了降阶建模方法,使快速参数估计研究。通过在LIGO算法库中实现一个降阶求积方案,我们表明,对于早期先进探测器的配置,对非自旋双星中子星吸气的9维参数空间的贝叶斯推断可以加快30倍。随着探测器将其低频限制提高到10 Hz,这种速度将增加到大约$150$,从而将需要数月才能完成的分析减少到数小时。虽然这些结果集中在引力探测器,技术广泛适用于任何实验中快速贝叶斯分析是可取的。

英文摘要:

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 parameter estimation approaches for such scenarios can lead to computationally intractable problems in practice. 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 studies. By implementing a reduced order quadrature scheme within the LIGO Algorithm Library, we show that Bayesian inference on the 9-dimensional parameter space of non-spinning binary neutron star inspirals can be sped up by a factor of 30 for the early advanced detectors' configurations. This speed-up will increase to about $150$ as the detectors improve their low-frequency limit to 10Hz, reducing to hours analyses which would otherwise take months to complete. Although these results focus on gravitational detectors, the techniques are broadly applicable to any experiment where fast Bayesian analysis is desirable.

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