We present a new method which accounts for changes in the properties of gravitational-wave detector noise over time in the PyCBC search for gravitational waves from compact binary coalescences. We use information from LIGO data quality streams that monitor the status of each detector and its environment to model changes in the rate of noise in each detector. These data quality streams allow candidates identified in the data during periods of detector malfunctions to be more efficiently rejected as noise. This method allows data from machine learning predictions of the detector state to be included as part of the PyCBC search, increasing the the total number of detectable gravitational-wave signals by up to 5%. When both machine learning classifications and manually-generated flags are used to search data from LIGO-Virgo's third observing run, the total number of detectable gravitational-wave signals is increased by up to 20% compared to not using any data quality streams. We also show how this method is flexible enough to include information from large numbers of additional arbitrary data streams that may be able to further increase the sensitivity of the search.
The third observing run of Advanced LIGO and Advanced Virgo took place between April 2019 and March 2020 and resulted in dozens of gravitational-wave candidates, many of which are now published as confident detections. A crucial requirement of the third observing run has been the rapid identification and public reporting of compact binary mergers, which enabled massive followup observation campaigns with electromagnetic and neutrino observatories. PyCBC Live is a low-latency search for compact binary mergers based on frequency-domain matched filtering, which has been used during the second and…
"Blip glitches" are a type of short duration transient noise in LIGO data. The cause for the majority of these is currently unknown. Short duration transient noise creates challenges for searches of the highest mass binary black hole systems, as standard methods of applying signal consistency, which look for consistency in the accumulated signal-to-noise of the candidate event, are unable to distinguish many blip glitches from short duration gravitational-wave signals due to similarities in their time and frequency evolution. We demonstrate a straightforward method, employed during Advanced LI…
We describe the PyCBC search for gravitational waves from compact-object binary coalescences in advanced gravitational-wave detector data. The search was used in the first Advanced LIGO observing run and unambiguously identified two black hole binary mergers, GW150914 and GW151226. At its core, the PyCBC search performs a matched-filter search for binary merger signals using a bank of gravitational-wave template waveforms. We provide a complete description of the search pipeline including the steps used to mitigate the effects of noise transients in the data, identify candidate events and meas…
The worldwide advanced gravitational-wave (GW) detector network has so far primarily consisted of the two Advanced LIGO observatories at Hanford and Livingston, with Advanced Virgo joining the 2016-7 O2 observation run at a relatively late stage. However Virgo has been observing alongside the LIGO detectors since the start of the O3 run; in the near future, the KAGRA detector will join the global network and a further LIGO detector in India is under construction. Gravitational-wave search methods would therefore benefit from the ability to analyse data from an arbitrary network of detectors. I…
Matched-filter based PyCBC searches have successfully detected $\sim \mathcal{O}(50)$ compact binary merger signals in the LIGO-Virgo data. While most PyCBC searches have been designed to detect stellar-mass binaries, we present here a new search that is optimised to detect short-duration \ac{GW} signals emitted by intermediate-mass black hole mergers. When applied to the first half of the third observation run data, the optimised search re-identified the \ac{IMBH} binary event, GW190521, with a false alarm rate of 1 in 727 yrs, significantly lower than the previous PyCBC search result of 1 in…
The advent of gravitational wave (GW) astronomy has provided us with observations of black holes more massive than those known from X-ray astronomy. However, the observation of an intermediate-mass black hole (IMBH) remains a big challenge. After their second observing run, the LIGO \& Virgo Scientific collaborations (LVC) placed upper limits on the coalescence rate density of non-precessing IMBH binaries (IMBHBs). In this article, we explore the sensitivity of two of the search pipelines used by the LVC to signals from 69 numerically simulated IMBHBs with generic spins, out of which 27 have a…
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。
Detection of many compact binary coalescences (CBCs) is one of the primary goals of the present and future ground-based gravitational-wave (GW) detectors. While increasing the detectors' sensitivities will be crucial in achieving this, efficient data analysis strategies can play a vital role. With given computational power in hand, efficient data analysis techniques can expand the size and dimensionality of the parameter space to search for a variety of GW sources. Matched filtering based analyses that depend on modeled signals to produce adequate signal-to-noise ratios for signal detection ma…
We introduce an efficient and straightforward technique for rapidly detecting gravitational waves from compact binary mergers. We show that this method achieves the low latencies required to alert electromagnetic partners of candidate binary mergers, aids in data monitoring, and makes use of multidetector networks for sky localization. This approach was instrumental to the analysis of gravitational-wave candidates during the second observing run of Advanced LIGO, including the period of coincident operation with Advanced Virgo, and in particular the analysis of the first observed binary neutro…
The detection of intermediate-mass black holes (IMBHs) i.e. those with mass $\sim 100$-$10^5 M_\odot$, is an emerging goal of gravitational-wave (GW) astronomy with wide implications for cosmology and tests of strong-field gravity. Current PyCBC-based searches for compact binary mergers, which matched filter the detector data against a set of template waveforms, have so far detected or confirmed several GW events. However, the sensitivity of these searches to signals arising from mergers of IMBH binaries is not optimal. Here, we present a new optimised PyCBC-based search for such signals. Our …
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…
We present a search for gravitational waves from merging binary neutron stars which have non-negligible eccentricity as they enter the LIGO observing band. We use the public Advanced LIGO data which covers the period from 2015 through 2017 and contains $\sim164$ days of LIGO-Hanford and LIGO-Livingston coincident observing time. The search was conducted using matched-filtering using the PyCBC toolkit. We find no significant binary neutron star candidates beyond GW170817, which has previously been reported by searches for binaries in circular orbits. We place a 90% upper limit of $\sim1700$ mer…
We investigate the use of particle swarm optimization (PSO) algorithm for detection of gravitational-wave signals from compact binary coalescences. We show that the PSO is fast and effective in searching for gravitational wave signals. The PSO-based aligned-spin coincident multi-detector search recovers appreciably more gravitational-wave signals, for a signal-to-noise ratio (SNR) of 10, the PSO based aligned-spin search recovers approximately 26 $\%$ more events as compared to the template bank searches. The PSO-based aligned-spin coincident search uses 48k matched-filtering operations, and p…
The possible formation of stellar-mass binary black holes through dynamical interactions in dense stellar environments predicts the existence of binaries with non-negligible eccentricity in the frequency band of ground-based gravitational wave detectors; the detection of binary black hole mergers with measurable orbital eccentricity would validate the existence of this formation channel. Waveform templates currently used in the matched-filter gravitational-wave searches of LIGO-Virgo data neglect effects of eccentricity which is expected to reduce their efficiency to detect eccentric binary bl…
Inter-University Centre for Astronomy and Astrophysics (IUCAA)（印度理工学院天文学和天体物理学跨大学中心）；Max-Planck-Institut für Gravitationsphysik (Albert-Einstein-Institut)（马克斯-普朗克引力物理研究所（阿尔伯特-爱因斯坦研究所））；Institut für Gravitationsphysik, Leibniz Universität Hannover（汉诺威莱布尼茨大学引力物理研究所）；Max Planck Institute for Gravitational Physics (Albert Einstein Institute)（马克斯·普朗克引力物理研究所（阿尔伯特·爱因斯坦研究所））
We present a novel Machine Learning (ML) based strategy to search for binary black hole (BBH) mergers in data from ground-based gravitational wave (GW) observatories. This is the first ML-based search that not only recovers all the compact binary coalescences (CBCs) in the first GW transients catalog (GWTC-1), but also makes a clean detection of GW151216 by only adding a new coincident ranking statistic (MLStat) to a standard analysis that was used for GWTC-1. In CBC searches, reducing contamination by terrestrial and instrumental transients, which create a loud noise background by triggering …
The LIGO observatories detect gravitational waves through monitoring changes in the detectors' length down to below $10^{-19}$\,$m/\sqrt{Hz}$ variation---a small fraction of the size of the atoms that make up the detector. To achieve this sensitivity, the detector and its environment need to be closely monitored. Beyond the gravitational wave data stream, LIGO continuously records hundreds of thousands of channels of environmental and instrumental data in order to monitor for possibly minuscule variations that contribute to the detector noise. A particularly challenging issue is the appearance…
Assuming that, for a given source of gravitational waves (GWs), we know its sky position, as is the case of GW events with an electromagnetic counterpart such as GW170817, we discuss a null stream method to probe GW polarizations including spin-0 (scalar) GW modes and spin-1 (vector) modes, especially with an expected network of Advanced LIGO, Advanced Virgo and KAGRA. For two independent null streams for four non-co-aligned GW detectors, we study a location on the sky, exactly at which the spin-0 modes of GWs vanish in any null stream for the GW detector network, though the strain output at a…
Advanced LIGO's raw detector output needs to be calibrated to compute dimensionless strain h(t). Calibrated strain data is produced in the time domain using both a low-latency, online procedure and a high-latency, offline procedure. The low-latency h(t) data stream is produced in two stages, the first of which is performed on the same computers that operate the detector's feedback control system. This stage, referred to as the front-end calibration, uses infinite impulse response (IIR) filtering and performs all operations at a 16384 Hz digital sampling rate. Due to several limitations, this p…
General metric theories of gravity in four-dimensional spacetimes can contain at most six polarization modes (two spin-0, two spin-1 and two spin-2) of gravitational waves (GWs). It has been recently shown that, with using four GW non-coaligned detectors, a direct test of the spin-1 modes can be done in principle separately from the spin-0 and spin-2 modes for a GW source in particular sky positions [Hagihara et al., Phys. Rev. D 98, 064035 (2018)]. They have found particular sky positions that satisfy a condition of killing completely the spin-0 modes in a so-called null stream which is a lin…
The Fermi collaboration identified a possible electromagnetic counterpart of the gravitational wave event of September 14, 2015. Our goal is to provide an unsupervised data analysis algorithm to identify similar events in Fermi's Gamma-ray Burst Monitor CTTE data stream. We are looking for signals that are typically weak. Therefore, they can only be found by a careful analysis of count rates of all detectors and energy channels simultaneously. Our Automatized Detector Weight Optimization (ADWO) method consists of a search for the signal, and a test of its significance. We developed ADWO, a vir…
The Omicron software is a tool developed to perform a multi-resolution time-frequency analysis of data from gravitational-wave detectors: the LIGO, Virgo, and KAGRA detectors. Omicron generates spectrograms from whitened data streams, offering a visual representation of transient detector noises and gravitational-wave events. In addition, these events can be parameterized with an optimized resolution. They can be written to disk to conduct offline noise characterization and gravitational-wave event validation studies. Omicron is optimized to process, in parallel, thousands of data streams reco…
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…
Status of the joint LIGO--TAMA300 inspiral analysis
本文介绍了2003年2月14日至4月14日LIGO和TAMA在LIGO Science Run 2和TAMA 300 Data Taking Run 8数据中对吸入中子星双星的引力波的联合搜索情况。在本文中，我们讨论了从分析作为“游乐场”保留的数据样本的子集中学到了什么。我们通过注入模拟的银河系双星中子星信号到数据流中，确定了合并时间和啁啾质量等参数的符合条件。我们选择符合条件，以最大限度地提高我们的效率检测模拟信号。我们获得的效率为78%，我们的重合搜索，并表明，我们错过了主要是非常遥远的信号TAMA 300。我们进行时间滑动分析，以估计由于意外巧合的噪声触发的背景。我们发现，背景触发有一个非常不同的字符从模拟信号的触发。
We develop a new multi-detector signal-based discriminator to improve the sensitivity of searches for gravitational waves from compact binary coalescences. The new statistic is the traditional $χ^2$ computed on a null-stream synthesized from the gravitational-wave detector strain time-series of three detectors. This null-stream-$χ^2$ statistic can be extended to networks involving more than three detectors as well. The null-stream itself was proposed as a discriminator between correlated unmodeled signals in multiple detectors, such as arising from a common astrophysical source, and uncorrelat…
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 …
We adopt the Pearson cross-correlation measure to analyze the LIGO Hanford and LIGO Livingston detector data streams around the events GW150914, GW151012,GW151226 and GW170104. We find that the Pearson cross-correlation method is sensitive to these signals, with correlations peaking when the black hole binaries reconstructed by the LIGO Scientific and Virgo Collaborations, are merging. We compare the obtained cross-correlations with the statistical correlation fluctuations arising in simulated Gaussian noise data and in LIGO data at times when no event is claimed. Our results for the significa…
We present the results of a cross-correlation search for gravitational waves from SNR 1987A using the second year of LIGO Science Run 5 data. The frequency band 75--450\,Hz is searched. No evidence of gravitational waves is found. A 90\% confidence upper limit of $h_0 \leq 3.8\times10^{-25}$ is placed on the gravitational wave strain at the most sensitive frequency near 150\,Hz. This corresponds to an ellipticity of $ε\leq 8.2\times10^{-4}$ and improves on previously published strain upper limits by a factor $\approx4$. We perform a comprehensive suite of validations of the search algorithm an…
We present a null-stream-based Bayesian unmodeled framework to probe generic gravitational-wave polarizations. Generic metric theories allow six gravitational-wave polarization states, but general relativity only permits the existence of two of them namely the tensorial polarizations. The strain signal measured by an interferometer is a linear combination of the polarization modes and such a linear combination depends on the geometry of the detector and the source location. The detector network of Advanced LIGO and Advanced Virgo allows us to measure different linear combinations of the polari…
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 recent Nobel-prize-winning detections of gravitational waves from merging black holes and the subsequent detection of the collision of two neutron stars in coincidence with electromagnetic observations have inaugurated a new era of multimessenger astrophysics. To enhance the scope of this emergent science, we proposed the use of deep convolutional neural networks for the detection and characterization of gravitational wave signals in real-time. This method, Deep Filtering, was initially demonstrated using simulated LIGO noise. In this article, we present the extension of Deep Filtering usi…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；NCSA（美国国家超级计算应用中心）
The recent Nobel-prize-winning detections of gravitational waves from merging black holes and the subsequent detection of the collision of two neutron stars in coincidence with electromagnetic observations have inaugurated a new era of multimessenger astrophysics. To enhance the scope of this emergent field of science, we pioneered the use of deep learning with convolutional neural networks, that take time-series inputs, for rapid detection and characterization of gravitational wave signals. This approach, Deep Filtering, was initially demonstrated using simulated LIGO noise. In this article, …
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…
After their successful first observing run (September 12, 2015 - January 12, 2016), the Advanced LIGO detectors were upgraded to increase their sensitivity for the second observing run (November 30, 2016 - August 26, 2017). The Advanced Virgo detector joined the second observing run on August 1, 2017. We discuss the updates that happened during this period in the GstLAL-based inspiral pipeline, which is used to detect gravitational waves from the coalescence of compact binaries both in low latency and an offline configuration. These updates include deployment of a zero-latency whitening filter…
The sensitivity of gravitational wave searches for binary black holes is estimated via the injection and posterior recovery of simulated gravitational wave signals in the detector data streams. When a search reports no detections, the estimated sensitivity is then used to place upper limits on the coalescence rate of the target source. In order to obtain correct sensitivity and rate estimates, the injected waveforms must be faithful representations of the real signals. Up to date, however, injected waveforms have neglected radiation modes of order higher than the quadrupole, potentially biasin…
Convolutional Neural Networks for the classification of glitches in gravitational-wave data streams
Universidade de Aveiro（阿威罗大学）；Centro de Física das Universidades do Minho e do Porto (CF-UM-UP)（米尼奥大学和波尔图大学物理中心（CF-UM-UP））；Universidade do Minho（米尼奥大学）；Instituto Galego de Física de Altas Enerxías（加利西亚高能物理研究所）；Universidade de Santiago de Compostela（圣地亚哥德孔波斯特拉大学）；Department of Physics, The Chinese University of Hong Kong（香港中文大学物理系）；Universitat de València（瓦伦西亚大学）
As LIGO and Virgo are upgraded, improving calibration systems to keep pace with the anticipated signal-to-noise enhancements will be challenging. We explore here a calibration method that uses astronomical signals, namely inspiral signals from compact-object binaries, and we show that it can in principle enable calibration at the sub-1\% accuracy levels needed for future gravitational wave science. We show how ensembles of these transient events can be used to measure the calibration errors of individual detectors in a network of three or more comparably sensitive instruments. As with telescop…
Argonne National Laboratory（阿贡国家实验室）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Chicago（芝加哥大学）
We introduce an ensemble of artificial intelligence models for gravitational wave detection that we trained in the Summit supercomputer using 32 nodes, equivalent to 192 NVIDIA V100 GPUs, within 2 hours. Once fully trained, we optimized these models for accelerated inference using NVIDIA TensorRT. We deployed our inference-optimized AI ensemble in the ThetaGPU supercomputer at Argonne Leadership Computer Facility to conduct distributed inference. Using the entire ThetaGPU supercomputer, consisting of 20 nodes each of which has 8 NVIDIA A100 Tensor Core GPUs and 2 AMD Rome CPUs, our NVIDIA Tens…
In this article, we revisit the problem of coherent multi-detector search of gravitational wave from compact binary coalescence with Neutron stars and Black Holes using advanced interferometers like LIGO-Virgo. Based on the loss of optimal multi-detector signal-to-noise ratio (SNR), we construct a hybrid statistic as a best of maximum-likelihood-ratio(MLR) statistic tuned for face-on and face-off binaries. The statistical properties of the hybrid statistic is studied. The performance of this hybrid statistic is compared with that of the coherent MLR statistic for generic inclination angles. Ow…
Joint gravitational-wave detections of stellar-mass black-hole binaries by ground- and space-based observatories will provide unprecedented opportunities for fundamental physics and astronomy. We present a semianalytic method to estimate multiband event rates by combining selection effects of ground-based interferometers (like LIGO/Virgo) and space missions (like LISA). We forecast the expected number of multiband detections first by using information from current LIGO/Virgo data, and then through population synthesis simulations of binary stars. We estimate that few to tens of LISA detections…
Multivariate Time Series Clustering for Environmental State Characterization of Ground-Based Gravitational-Wave Detectors
Computer Science \& Engineering University of California, Riverside Riverside, CA, USA；Physics University of Dallas Dallas, TX, USA；Physics \& Astronomy University of California, Riverside Riverside, CA, USA；LIGO Laboratory LIGO Livingston Observatory Livingston, LA, USA
