Enhancing the sensitivity of transient gravitational wave searches with Gaussian mixture models
Enhancing the sensitivity of transient gravitational wave searches with Gaussian mixture models
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DOI：
10.1103/physrevd.102.104023
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发表时间：
2020-08
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影响因子：
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通讯作者：
V. Gayathri;D. Lopez;Pranjal R. S.-Pranjal-R. S.-2126277510;I. Heng;A. Pai;C. Messenger
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作者：
V. Gayathri;D. Lopez;Pranjal R. S.-Pranjal-R. S.-2126277510;I. Heng;A. Pai;C. Messenger
识别埋在非平稳、非高斯噪声中的瞬态引力波的存在是一项非常具有挑战性的任务，这些噪声通常包含伪噪声瞬态（小故障）。对于给定的数据集，瞬态引力波搜索产生相应的触发列表，这些触发列表表明可能存在引力波信号。这些触发器通常是模仿引力波信号特征的小故障的结果。为了区分小故障和真正的引力波信号，搜索算法估计一系列触发属性，并对这些触发属性应用阈值，以将信号与噪声分开。在这里，我们提出使用高斯混合模型，一种监督机器学习方法，作为多维触发属性空间建模的手段。我们通过将其应用于LIGO O1数据中相干波暴搜索的触发器来证明这种方法。通过建立信号和背景噪声属性空间的高斯混合模型，我们表明，我们可以显着提高相干波突发搜索的灵敏度，并强烈抑制故障和背景噪声的影响，而无需使用原始O1搜索所使用的多个搜索箱。我们的研究表明，探测概率提高了10倍，从而提高了GW150914等引力波信号的统计显著性。
Identifying the presence of a gravitational wave transient buried in non-stationary, non-Gaussian noise which can often contain spurious noise transients (glitches) is a very challenging task. For a given data set, transient gravitational wave searches produce a corresponding list of triggers that indicate the possible presence of a gravitational wave signal. These triggers are often the result of glitches mimicking gravitational wave signal characteristics. To distinguish glitches from genuine gravitational wave signals, search algorithms estimate a range of trigger attributes, with thresholds applied to these trigger properties to separate signal from noise. Here, we present the use of Gaussian mixture models, a supervised machine learning approach, as a means of modelling the multi-dimensional trigger attribute space. We demonstrate this approach by applying it to triggers from the coherent Waveburst search for generic bursts in LIGO O1 data. By building Gaussian mixture models for the signal and background noise attribute spaces, we show that we can significantly improve the sensitivity of the coherent Waveburst search and strongly suppress the impact of glitches and background noise, without the use of multiple search bins as employed by the original O1 search. We show that the detection probability is enhanced by a factor of 10, leading enhanced statistical significance for gravitational wave signals such as GW150914.