论文标题
词典学习在Denoise Ligo的Blip噪声瞬变中的应用
Application of dictionary learning to denoise LIGO's blip noise transients
论文作者
论文摘要
引力波检测器的数据流受到工具和环境起源的瞬态噪声特征或“故障”的污染。在这项工作中，我们调查了总变化方法和学习词典的使用来减轻数据中这些瞬变的影响。我们专注于特定类型的瞬态“ blip”故障，因为这是Ligo探测器中最常见的小故障类型，它们的波形易于识别。我们随机选择80个Blip Glitches，这些小故障分散在Advanced Ligo的O1运行数据中，这是公民科学项目Gravity Spy提供的。我们的结果表明，在所有分析的情况下，词典学习方法是模型和减去大多数故障贡献的有效方法，尤其是在低于$ \ sim 1 $ kHz的频率下。当使用具有不同原子长度的词典的组合时，最好将故障的高频组成部分去除。作为另一个例子，我们将方法应用于在二进制中子恒星信号GW170817合并时在Ligo-Livingston数据中可见的故障，发现令人满意的结果。本文是我们正在进行的计划中的第一步，即使用各种方法自动对引力波小故障的所有家族进行分类和减去。
Data streams of gravitational-wave detectors are polluted by transient noise features, or "glitches", of instrumental and environmental origin. In this work, we investigate the use of total-variation methods and learned dictionaries to mitigate the effect of those transients in the data. We focus on a specific type of transient, "blip" glitches, as this is the most common type of glitch present in the LIGO detectors and their waveforms are easy to identify. We randomly select 80 blip glitches scattered in the data from advanced LIGO's O1 run, as provided by the citizen-science project Gravity Spy. Our results show that dictionary-learning methods are a valid approach to model and subtract most of the glitch contribution in all cases analyzed, particularly at frequencies below $\sim 1$ kHz. The high-frequency component of the glitch is best removed when a combination of dictionaries with different atom length is employed. As a further example, we apply our approach to the glitch visible in the LIGO-Livingston data around the time of merger of binary neutron star signal GW170817, finding satisfactory results. This paper is the first step in our ongoing program to automatically classify and subtract all families of gravitational-wave glitches employing variational methods.
