引力波数据分析中rROF去噪方法在cWB流水线中的实现
Implementation of the rROF denoising method in the cWB pipeline for gravitational-wave data analysis
摘要：
由当前的引力波探测器网络收集的数据在很大程度上是由仪器噪声控制的。基于L1范数最小化的全变分方法是近年来提出的一种有效的引力波数据去噪方法。特别是，正则化的Rudin-Osher-Fatemi（rROF）模型已被证明可以有效地对嵌入在模拟高斯噪声或实际检测器噪声中的信号进行降噪。因此，将rROF模型扩展到现有的搜索管道似乎值得考虑。在本文中，我们讨论了两个变种的rROF算法作为两个独立的插件的相干波突发（cWB）管道设计进行搜索的未建模的引力波突发源的实施。第一种方法是基于单步rROF方法，第二种方法采用迭代rROF过程。这两种方法都是使用实际的引力波事件从前三个观测运行的LIGO-Virgo-KAGRA合作，即GW 1501914，GW 151226，GW 170817和GW 190521，包括不同类型的紧凑的二元聚结。我们的分析表明，在cWB流水线中实现的rROF去噪算法的迭代版本有效地消除了噪声，同时保持波形信号的完整性。因此，组合方法产生比在没有rROF去噪步骤的情况下由cWB流水线计算的信噪比值更高的信噪比值。因此，在cWB流水线中结合迭代rROF算法可能会影响流水线的检测能力以及源属性的推断。
英文摘要：
The data collected by the current network of gravitational-wave detectors are largely dominated by instrumental noise. Total variation methods based on L1-norm minimization have recently been proposed as a powerful technique for noise removal in gravitational-wave data. In particular, the regularized Rudin-Osher-Fatemi (rROF) model has proven effective to denoise signals embedded in either simulated Gaussian noise or actual detector noise. Importing the rROF model to existing search pipelines seems therefore worth considering. In this paper, we discuss the implementation of two variants of the rROF algorithm as two separate plug-ins of the coherent Wave Burst (cWB) pipeline designed to conduct searches of unmodelled gravitational-wave burst sources. The first approach is based on a single-step rROF method and the second one employs an iterative rROF procedure. Both approaches are calibrated using actual gravitational-wave events from the first three observing runs of the LIGO-Virgo-KAGRA collaboration, namely GW1501914, GW151226, GW170817, and GW190521, encompassing different types of compact binary coalescences. Our analysis shows that the iterative version of the rROF denoising algorithm implemented in the cWB pipeline effectively eliminates noise while preserving the waveform signals intact. Therefore, the combined approach yields higher signal-to-noise values than those computed by the cWB pipeline without the rROF denoising step. The incorporation of the iterative rROF algorithm in the cWB pipeline might hence impact the detectability capabilities of the pipeline along with the inference of source properties.
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