Application of machine learning algorithms to the study of noise artifacts in gravitational-wave data
Rahul Biswas, Lindy Blackburn, Junwei Cao, Reed Essick, Kari Alison Hodge, Erotokritos Katsavounidis, Kyungmin Kim, Young-Min Kim, Eric-Olivier Le Bigot, Chang-… 展开作者
Rahul Biswas, Lindy Blackburn, Junwei Cao, Reed Essick, Kari Alison Hodge, Erotokritos Katsavounidis, Kyungmin Kim, Young-Min Kim, Eric-Olivier Le Bigot, Chang-Hwan Lee, John J. Oh, Sang Hoon Oh, Edwin J. Son, Ruslan Vaulin, Xiaoge Wang, Tao Ye
The sensitivity of searches for astrophysical transients in data from the LIGO is generally limited by the presence of transient, non-Gaussian noise artifacts, which occur at a high-enough rate such that accidental coincidence across multiple detectors is non-negligible. Furthermore, non-Gaussian noise artifacts typically dominate over the background contributed from stationary noise. These "glitches" can easily be confused for transient gravitational-wave signals, and their robust identification and removal will help any search for astrophysical gravitational-waves. We apply Machine Learning Algorithms (MLAs) to the problem, using data from auxiliary channels within the LIGO detectors that monitor degrees of freedom unaffected by astrophysical signals. The number of auxiliary-channel parameters describing these disturbances may also be extremely large; an area where MLAs are particularly well-suited. We demonstrate the feasibility and applicability of three very different MLAs: Artificial Neural Networks, Support Vector Machines, and Random Forests. These classifiers identify and remove a substantial fraction of the glitches present in two very different data sets: four weeks of LIGO's fourth science run and one week of LIGO's sixth science run. We observe that all three algorithms agree on which events are glitches to within 10% for the sixth science run data, and support this by showing that the different optimization criteria used by each classifier generate the same decision surface, based on a likelihood-ratio statistic. Furthermore, we find that all classifiers obtain similar limiting performance, suggesting that most of the useful information currently contained in the auxiliary channel parameters we extract is already being used.
Denoising gravitational-wave signals from binary black holes with dilated convolutional autoencoder
引力波探测器的宽带频率输出是一种非平稳、非高斯的时间序列数据流，其中包含与引力波信号同时间尺度演化的局部扰动和瞬态伪影，可能会破坏天体物理信息。我们研究了一种去噪算法，致力于通过在编码器-解码器配置中采用卷积神经网络来暴露天体物理信号，即在公开可用的LIGO O 1时间序列应变数据中应用合并二进制黑洞信号的去噪过程。去噪卷积自动编码器神经网络在注入真实探测器噪声的模拟天体物理信号数据集和探测器噪声伪影（“毛刺”）数据集上进行训练，并在O 1和O2 LIGO-Virgo观测运行的真实引力波事件上测试其保真度。
A new veto for continuous gravitational wave searches
我们提出了一个新的否决程序来区分连续的引力波（CW）信号和探测器的文物，可以模仿他们的行为。否决程序利用了这样一个事实，即一个长期的相干扰动是不太可能比一个真正的信号表现出天体物理起源的多普勒调制。因此，在搜索中存在离群值的情况下，我们在多普勒调制关闭（DM关闭）的情况下围绕离群值的频率执行多步搜索，并将这些结果与原始（DM打开）搜索的结果进行比较。如果来自DM关闭搜索的结果比来自DM打开搜索的结果更显著，则离群值最有可能是由于伪影而不是信号。我们调整否决程序，使其具有非常低的错误驳回率。有了这个否决权，我们能够在最近的全天低频Einstein@Home搜索中识别出6349个候选者中的99.9%以上的相干扰动，这些候选者来自Advanced LIGO O 1观测运行的数据[1]。我们在附录中提供了每种已识别干扰的详细信息。
Machine-learning non-stationary noise out of gravitational wave detectors
California Institute of Technology（加州理工学院）；LIGO Laboratory（激光干涉引力波天文台实验室）；Massachusetts Institute of Technology（麻省理工学院）；Kavli Institute for Astrophysics and Space Research（卡夫利天体物理与空间研究所）；LIGO Hanford Observatory（激光干涉引力波天文台汉福德观测站）；University of Florida（佛罗里达大学）
