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 describes the methods to identify glitches, the decision process for deciding if mitigation was necessary, and the two algorithms, BayesWave and gwsubtract, that were used to model and subtract glitches. Through case studies of two events, GW190424_180648 and GW200129_065458, we evaluate the effectiveness of the glitch subtraction, compare the statistical uncertainties in the relevant glitch models, and identify potential limitations in these glitch subtraction methods. We finally outline the lessons learned from this first-of-its-kind effort for future observing runs.
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…
We present a computational method to identify glitches in gravitational-wave data that occur nearby gravitational-wave signals from compact binary coalescences. The Q-transform, an established tool in LIGO-Virgo-KAGRA data analysis, computes the probability of any excess in the data surrounding a signal against the assumption of a Gaussian noise background, flagging any significant glitches. Subsequently, we perform validation tests on this computational method to ensure self-consistency in colored Gaussian noise, as well as data that contains a gravitational-wave event after subtracting the s…
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）
Gravitational-wave (GW) data contains non-Gaussian noise transients called "glitches". During the third LIGO-Virgo observing run about 24% of all gravitational-wave candidates were in the vicinity of a glitch, while even more events could be affected in future observing runs due to increasing detector sensitivity. This poses a problem since glitches can affect the estimation of GW source parameters, including sky localisation, which is crucial to identify an electromagnetic (EM) counterpart. In this paper we present a study that estimates how much sky localisation is affected by a nearby glitc…
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…
Measuring the rate of glitches in interferometric gravitational wave detectors with a hierarchical Bayesian model
University of Southampton（南安普顿大学）；Royal Holloway, University of London（伦敦大学皇家霍洛威学院）；Princeton University（普林斯顿大学）；Institut de Física d’Altes Energies（高能物理研究所）；Institució Catalana de Recerca i Estudis Avançats（加泰罗尼亚高级研究学院）；California State University Fullerton（加州州立大学富勒顿分校）
Automatic classification pipeline for glitches in the Virgo detector
Centro de Física das Universidades do Minho e do Porto (CF-UM-UP), Universidade do Minho（明道大学和波尔图大学物理中心）；Universidade do Minho（明道大学）；Departamento de Astronomía y Astrofísica, Universitat de València（瓦伦西亚大学天文学与天体物理学系）；Universitat de València（瓦伦西亚大学）；Università di Firenze（佛罗伦萨大学）；INFN, Sezione di Firenze（意大利国家核物理研究所佛罗伦萨分部）；Centro de Física das Universidades do Minho e do Porto (CF-UM-UP)（明道大学和波尔图大学物理中心）；Observatorio Astronómico, Universitat de València（瓦伦西亚大学天文台）
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）
SiGMa-Net II: Distinguishing Binary Black Holes from Glitches
Indian Institute of Science Education and Research, Pune（印度科学教育研究学院浦那分校）；Aix Marseille Univ, CNRS/IN2P3, CPPM, Marseille（艾克斯马赛大学，法国国家科学研究中心/法国国家粒子物理研究所，普罗旺斯中心亚原子物理研究所马赛）；Inter-University Centre for Astronomy and Astrophysics（大学间天文学与天体物理学中心）；Kavli Institute for the Physics and Mathematics of the Universe (IPMU)（宇宙物理与数学卡弗里研究所）；Department of Physics, University of Western Australia（西澳大利亚大学物理系）；Nicolaus Copernicus Astronomical Center, Polish Academy of Sciences（波兰科学院尼古拉·哥白尼天体生物学中心）；Department of Physics & Astronomy, Washington State University（华盛顿州立大学物理与天文系）
SiGMa-Net II 使用 SGP 地图和神经网络区分二进制黑洞与多种 glitches，提高引力波事件识别效率。
Time-Domain Synthesis of Gravitational-Wave Detector Glitches using Class-Conditional Derivative Generative Adversarial Networks
Nikhef（荷兰国家核子与亚原子物理研究所）；Institute for Gravitational and Subatomic Physics (GRASP), Utrecht University（乌得勒支大学引力与亚原子物理研究所（GRASP））；Faculty of Science, Open Universiteit（荷兰开放大学理学院）
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…
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…
This paper presents an adaptable, parallelizable method for subtracting linearly coupled noise from Advanced LIGO data. We explain the features developed to ensure that the process is robust enough to handle the variability present in Advanced LIGO data. In this work, we target subtraction of noise due to beam jitter, detector calibration lines, and mains power lines. We demonstrate noise subtraction over the entirety of the second observing run, resulting in increases in sensitivity comparable to those reported in previous targeted efforts. Over the course of the second observing run, we see …
Polytechnic University of the Philippines（菲律宾理工大学）
LIGO interferometer is considered the most sensitive and complicated gravitational experimental equipment ever built. Its main objective is to detect the gravitational wave from the strongest events in the universe by observing if the length of its 4-kilometer arms change by a distance 10,000 times smaller than the diameter of a proton. Due to its sensitivity, interferometer is prone to the disturbance of external noises which affects the data being collected to detect the gravitational wave. These noises are commonly called by the gravitational-wave community as glitches. This study focuses o…
The LiteBIRD satellite is planned to be launched by JAXA in the late 2020s. Its main purpose is to observe the large-scale B-mode polarization in the Cosmic Microwave Background (CMB) anticipated from the Inflation theory. LiteBIRD will observe the sky for three years at the second Lagrangian point (L2) of the Sun-Earth system. Planck was the predecessor for observing the CMB at L2, and the onboard High Frequency Instrument (HFI) suffered contamination by glitches caused by the cosmic-ray (CR) hits. We consider the CR hits can also be a serious source of the systematic uncertainty for LiteBIRD…
(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 …
Broadband frequency output of gravitational-wave detectors is a non-stationary and non-Gaussian time series data stream dominated by noise populated by local disturbances and transient artifacts, which evolve on the same timescale as the gravitational-wave signals and may corrupt the astrophysical information. We study a denoising algorithm dedicated to expose the astrophysical signals by employing a convolutional neural network in the encoder-decoder configuration, i.e. apply the denoising procedure of coalescing binary black hole signals in the publicly available LIGO O1 time series strain d…
Ground-based observations at thermal infrared wavelengths suffer from large background radiation due to the sky, telescope and warm surfaces in the instrument. This significantly limits the sensitivity of ground-based observations at wavelengths longer than 3 microns. We analyzed this background emission in infrared high contrast imaging data, show how it can be modelled and subtracted and demonstrate that it can improve the detection of faint sources, such as exoplanets. We applied principal component analysis to model and subtract the thermal background emission in three archival high contra…
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…
PyCBC Live Search for Compact Binary Mergers in Advanced LIGO and Virgo's Fourth Observing Run
University of Maryland, College Park（马里兰大学帕克分校）；Institute for Cosmology and Gravitation, University of Portsmouth（朴茨茅斯宇宙学与引力研究所，朴茨茅斯大学）；Université Paris-Saclay, CNRS/IN2P3, IJCLab（巴黎-萨克雷大学，法国国家科学研究中心/欧洲核子研究学院，IJCLab）；Instituto Galego de Física de Altas Enerxías, Universidade de Santiago de Compostela（加利西亚高能物理研究所，圣地亚哥-德孔波斯特拉大学）
本文改进了 PyCBC Live 低延迟搜索管道，用于 LIGO-Virgo-KAGRA 第四次观测运行（O4），通过增强排名统计量、优化信噪比恢复和实现预警搜索，将灵敏度提高 1.7-2.3 倍，并实现 2.5-3.5 秒延迟的并合前预警。
The observation of gravitational waves is hindered by the presence of transient noise (glitches). We study data from the third observing run of the Advanced LIGO detectors, and identify new glitch classes. Using training sets assembled by monitoring of the state of the detector, and by citizen-science volunteers, we update the Gravity Spy machine-learning algorithm for glitch classification. We find that a new glitch class linked to ground motion at the detector sites is especially prevalent, and identify two subclasses of this linked to different types of ground motion. Reclassification of da…
Astrophysical or Terrestrial: Machine learning classification of gravitational-wave candidates using multiple-search information
University of Minnesota（明尼苏达大学）；The University of British Columbia（不列颠哥伦比亚大学）；NASA Goddard Space Flight Center（美国国家航空航天局戈达德太空飞行中心）；Joint Space-Science Institute, University of Maryland（马里兰大学联合空间科学研究所）
We introduce a new technique to search for gravitational wave events from compact binary mergers that produce a clear signal only in a single gravitational wave detector, and marginal signals in other detectors. Such a situation can arise when the detectors in a network have different sensitivities, or when sources have unfavorable sky locations or orientations. We start with a short list of loud single-detector triggers from regions of parameter space that are empirically unaffected by glitches (after applying signal-quality vetoes). For each of these triggers, we compute evidence for astroph…
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Max Planck ETH Center for Learning Systems（马克斯·普朗克-苏黎世联邦理工学院学习系统中心）；ETH Zurich（苏黎世联邦理工学院）
High-contrast imaging of exoplanets hinges on powerful post-processing methods to denoise the data and separate the signal of a companion from its host star, which is typically orders of magnitude brighter. Existing post-processing algorithms do not use all prior domain knowledge that is available about the problem. We propose a new method that builds on our understanding of the systematic noise and the causal structure of the data-generating process. Our algorithm is based on a modified version of half-sibling regression (HSR), a flexible denoising framework that combines ideas from the field…
