This paper presents an analysis of noise transients observed in LIGO data during the first part of the fourth observing run, using the unsupervised machine learning technique t-distributed Stochastic Neighbor Embedding (t-SNE) to examine the behavior of glitch groups. Based on the t-SNE output, we apply Agglomerative Clustering in combination with the Silhouette Score to determine the optimal number of groups. We then track these groups over time and investigate correlations between their occurrence and environmental or instrumental conditions. At the Livingston observatory, the most common glitches during O4a were seasonal and associated with ground motion, whereas at Hanford, the most prevalent glitches were related to instrumental conditions.
Understanding the noise in gravitational-wave detectors is central to detecting and interpreting gravitational-wave signals. Glitches are transient, non-Gaussian noise features that can have a range of environmental and instrumental origins. The Gravity Spy project uses a machine-learning algorithm to classify glitches based upon their time-frequency morphology. The resulting set of classified glitches can be used as input to detector-characterisation investigations of how to mitigate glitches, or data-analysis studies of how to ameliorate the impact of glitches. Here we present the results of…
Hunting for new glitches in LIGO data using community science
University of Glasgow（格拉斯哥大学）；Gravity Spy（重力间谍）；ConSol Consulting & Solutions Software GmbH（ConSol咨询与解决方案软件有限公司）；Syracuse University（雪城大学）；LIGO Laboratory, California Institute of Technology（加州理工学院引力波观测站实验室）；Northwestern University（西北大学）；NSF-Simons AI Institute for the Sky (SkAI)（NSF-西蒙斯天空人工智能研究所）
In recent years, much work have studied the use of convolutional neural networks for gravitational-wave detection. However little work pay attention to whether the transient noise can trigger the CNN model or not. In this paper, we study the responses of the sine-Gaussian glitches, the Gaussian glitches and the ring-down glitches in the trained convolutional neural network classifier. We find that the network is robust to the sine-Gaussian and Gaussian glitches, whose false alarm probabilities are close to that of the LIGO-like noises, in contrast to the case of the ring-down glitches, in whic…
Assessing astrophysical foreground subtraction in DECIGO using compact binary populations inferred from the first part of the LIGO-Virgo-KAGRA's fourth observation run
Research Center for the Early Universe (RESCEU), Graduate School of Science, The University of Tokyo（东京大学理学研究科早期宇宙研究中心）
Excess noise from scattered light poses a persistent challenge in the analysis of data from gravitational wave detectors such as LIGO. We integrate a physically motivated model for the behavior of these "glitches" into a standard Bayesian analysis pipeline used in gravitational wave science. This allows for the inference of the free parameters in this model, and subtraction of these models to produce glitch-free versions of the data. We show that this inference is an effective discriminator of the presence of the features of these glitches, even when those features may not be discernible in st…
Data from ground-based gravitational-wave detectors contains numerous short-duration instrumental artifacts, called "glitches." The high rate of these artifacts in turn results in a significant fraction of gravitational-wave signals from compact binary coalescences overlapping glitches. In LIGO-Virgo's third observing run, $\approx 20\%$ of signals required some form of mitigation due to glitches. This was the first observing run that glitch subtraction was included as a part of LIGO-Virgo-KAGRA data analysis methods for a large fraction of detected gravitational-wave events. This work describ…
In the field of gravitational-wave (GW) interferometers, the most severe limitation to the detection of transient signals from astrophysical sources comes from transient noise artefacts, known as glitches, that happens at a rate around $1$ per minute. Because glitches reduce the amount of scientific data available, there is a need for better modelling and inclusion of glitches in large-scale studies, such as stress testing the search pipelines and increasing the confidence of detection. In this work, we employ a Generative Adversarial Network (GAN) to produce a particular class of glitches ({\…
Scattered light noise at LIGO Livingston Observatory during O4
Louisiana State University（路易斯安那州立大学）；LIGO Livingston Observatory（LIGO利文斯顿天文台）；University of California, Riverside（加州大学河滨分校）；University of Oregon（俄勒冈大学）
GSpyNetTree-O4: an event validation tool used in the fourth LIGO-Virgo-KAGRA observing run
LIGO Laboratory, Massachusetts Institute of Technology（麻省理工学院 LIGO 实验室）；Department of Physics and Astronomy, University of British Columbia（不列颠哥伦比亚大学物理与天文系）；Department of Physics, University of Konstanz（康斯坦茨大学物理系）；Departamento de Física, Universidad de los Andes（安第斯大学物理系）；Department of Computer Science, University of British Columbia（不列颠哥伦比亚大学计算机科学系）
NCSA（美国国家超级计算应用中心）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
The detection of gravitational waves with LIGO and Virgo requires a detailed understanding of the response of these instruments in the presence of environmental and instrumental noise. Of particular interest is the study of anomalous non-Gaussian noise transients known as glitches, since their high occurrence rate in LIGO/Virgo data can obscure or even mimic true gravitational wave signals. Therefore, successfully identifying and excising glitches is of utmost importance to detect and characterize gravitational waves. In this article, we present the first application of Deep Learning combined …
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
The exquisite sensitivity of the advanced LIGO detectors has enabled the detection of multiple gravitational wave signals. The sophisticated design of these detectors mitigates the effect of most types of noise. However, advanced LIGO data streams are contaminated by numerous artifacts known as glitches: non-Gaussian noise transients with complex morphologies. Given their high rate of occurrence, glitches can lead to false coincident detections, obscure and even mimic gravitational wave signals. Therefore, successfully characterizing and removing glitches from advanced LIGO data is of utmost i…
Advanced LIGO data contains numerous noise transients, or "glitches", that have been shown to reduce the sensitivity of matched filter searches for gravitational waves from compact binaries by increasing the rate at which random coincidences occur. The presence of these transients has precipitated extensive work to establish that observed gravitational wave events are astrophysical in nature. We discuss the response of the PyCBC search for gravitational waves from stellar mass binaries to various common glitches that were observed during Advanced LIGO's first and second observing runs. We show…
(abridged for arXiv) With the first direct detection of gravitational waves, the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) has initiated a new field of astronomy by providing an alternate means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as …
Data from current gravitational wave detectors contains a high rate of transient noise (glitches) that can trigger false detections and obscure true astrophysical events. Existing noise-detection algorithms largely rely on model-based methods that may miss noise transients unwitnessed by auxiliary sensors or with exotic morphologies. We propose the Unicorn Multi-window Anomaly-detection Pipeline (UniMAP): a model-free algorithm to identify and characterize transient noise leveraging the Temporal Outlier Factor (TOF) via a multi-window data-resampling scheme. We show this windowing scheme exten…
The noise of gravitational-wave (GW) interferometers limits their sensitivity and impacts the data quality, hindering the detection of GW signals from astrophysical sources. For transient searches, the most problematic are transient noise artifacts, known as glitches, that happen at a rate around $ 1 \text{ min}^{-1}$, and can mimic GW signals. Because of this, there is a need for better modeling and inclusion of glitches in large-scale studies, such as stress testing the pipelines. In this proof-of concept work we employ Generative Adversarial Networks (GAN), a state-of-the-art Deep Learning …
As of this moment, fifty gravitational waves (GW) detections have been announced, thanks to the observational efforts of the LIGO-Virgo Collaboration, working with the Advanced LIGO and the Advanced Virgo interferometers. The detection of signals is complicated by the noise-dominated nature of the data. Conventional approaches in GW detection procedures require either precise knowledge of the GW waveform in the context of matched filtering searches or coincident analysis of data from multiple detectors. Furthermore, the analysis is prone to contamination by instrumental or environmental artifa…
DeepExtractor: Time-domain reconstruction of signals and glitches in gravitational wave data with deep learning
Faculty of Science, Open Universiteit（科学学院，开放大学）；Institute for Gravitational and Subatomic Physics (GRASP), Utrecht University（引力与亚原子物理研究所（GRASP），乌得勒支大学）；Nikhef（Nikhef研究所）；Department of Physics, University of Massachusetts（物理系，马萨诸塞大学）
The Chinese University of Hong Kong（香港中文大学）；The Inter-University Centre for Astronomy and Astrophysics (IUCAA)（天文学与天体物理学大学间中心）；Kavli Institute for the Physics and Mathematics of the Universe (IPMU)（宇宙物理与数学系卡尔费利研究所）；Utrecht University（乌得勒支大学）；Nikhef – National Institute for Subatomic Physics（荷兰亚原子物理国家研究所）；Pennsylvania State University（宾夕法尼亚州立大学）；Universit'e Catholique de Louvain（天主教鲁汶大学）；Royal Observatory of Belgium（比利时皇家天文台）；Universitat de les Illes Balears（巴利阿里群岛大学）
The best of all worlds: gravitational-wave transient detection with multiple pipelines
Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）；Institute for Accelerated AI Algorithms for Data-Driven Discovery (A3D3), MIT（麻省理工学院数据驱动发现加速AI算法研究所）；University of Thessaly（色萨利大学）
Overlap reduction functions for a polarized stochastic gravitational-wave background in the Einstein Telescope-Cosmic Explorer and the LISA-Taiji networks
本文将基于球谐展开的重叠减少函数计算方法推广到第三代地基 Einstein Telescope-Cosmic Explorer 网络及 LISA-Taiji 空间联合任务，以分析极化随机引力波背景。
The field of gravitational-wave astronomy has been opened up by gravitational-wave observations made with interferometric detectors. This review surveys the current state-of-the-art in gravitational-wave detectors and data analysis methods currently used by the Laser Interferometer Gravitational-Wave Observatory in the United States and the Virgo Observatory in Italy. These analysis methods will also be used in the recently completed KAGRA Observatory in Japan. Data analysis algorithms are developed to target one of four classes of gravitational waves. Short duration, transient sources include…
In this paper, we report on the construction of a new and independent pipeline for analyzing the public data from the first observing run of advanced LIGO for mergers of compact binary systems. The pipeline incorporates different techniques and makes independent implementation choices in all its stages including the search design, the method to construct template banks, the automatic routines to detect bad data segments ("glitches") and to insulate good data from them, the procedure to account for the non-stationary nature of the detector noise, the signal-quality vetoes at the single-detector…
The recent detections of gravitational waves (GWs) reported by LIGO/Virgo collaborations have made significant impact on physics and astronomy. A global network of GW detectors will play a key role to solve the unknown nature of the sources in coordinated observations with astronomical telescopes and detectors. Here we introduce KAGRA (former name LCGT; Large-scale Cryogenic Gravitational wave Telescope), a new GW detector with two 3-km baseline arms arranged in the shape of an "L", located inside the Mt. Ikenoyama, Kamioka, Gifu, Japan. KAGRA's design is similar to those of the second generat…
Compact object mergers which produce both detectable gravitational waves and electromagnetic emission can provide valuable insights into the neutron star equation of state, the tension in the Hubble constant, and the origin of the r-process elements. However, electromagnetic follow-up of gravitational wave sources is complicated by false positive detections, sources which do not emit light, and the transient nature of the associated electromagnetic emission. GWSkyNet-Multi is a machine learning model that attempts to resolve these issues by providing real-time predictions of the source of a gr…
