Rapid and robust parameter inference for binary mergers
Rapid and robust parameter inference for binary mergers
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DOI：
10.1103/physrevd.103.104057
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发表时间：
2021-01
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影响因子：
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通讯作者：
N. Cornish
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文献类型：
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作者：
N. Cornish
随着全球地面引力波探测器网络灵敏度的提高，紧凑双星合并的探测率也在增长，现在已经达到了分析的强大自动化至关重要的阶段。已经开发了自动化的低延迟算法，当检测到候选信号时发出警报。警报包括有助于电磁跟踪观测的星图，沿着该系统可能包含中子星星的概率，因此更有可能产生电磁对应物。数据质量问题，如强噪声瞬变（毛刺），可能会对低延迟算法产生不利影响，导致错误警报并丢弃参数估计。这里提出了一种新的分析方法，该方法对故障具有鲁棒性，并且能够在几分钟内产生完全的贝叶斯参数推断，包括天空图和质量估计。该方法的关键要素是基于小波的去噪，惩罚最大化的可能性在初始搜索，快速天空定位使用预先计算的内积，和外差似然充分贝叶斯推理。
The detection rate for compact binary mergers has grown as the sensitivity of the global network of ground based gravitational wave detectors has improved, now reaching the stage where robust automation of the analyses is essential. Automated low-latency algorithms have been developed that send out alerts when candidate signals are detected. The alerts include sky maps to facilitate electromagnetic follow up observations, along with probabilities that the system might contain a neutron star, and hence be more likely to generate an electromagnetic counterpart. Data quality issues, such as loud noise transients (glitches), can adversely affect the low-latency algorithms, causing false alarms and throwing off parameter estimation. Here a new analysis method is presented that is robust against glitches, and capable of producing fully Bayesian parameter inference, including sky maps and mass estimates, in a matter of minutes. Key elements of the method are wavelet-based de-noising, penalized maximization of the likelihood during the initial search, rapid sky localization using pre-computed inner products, and heterodyned likelihoods for full Bayesian inference.