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Register a new userA guide to LIGO-Virgo detector noise and extraction of transient gravitational-wave signals
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The LIGO Scientific Collaboration and the Virgo Collaboration have cataloged eleven confidently detected gravitational-wave events during the first two observing runs of the advanced detector era. All eleven events were consistent with being from well-modeled mergers between compact stellar-mass objects: black holes or neutron stars. The data around the time of each of these events have been made publicly available through the gravitational-wave open science center. The entirety of the gravitational-wave strain data from the first and second observing runs have also now been made publicly available. There is considerable interest among the broad scientific community in understanding the data and methods used in the analyses. In this paper, we provide an overview of the detector noise properties and the data analysis techniques used to detect gravitational-wave signals and infer the source properties. We describe some of the checks that are performed to validate the analyses and results from the observations of gravitational-wave events. We also address concerns that have been raised about various properties of LIGO-Virgo detector noise and the correctness of our analyses as applied to the resulting data.
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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 compact binary coalescences, and burst sources originating from poorly modelled or unanticipated sources. Long duration sources include sources which emit continuous signals of consistent frequency, and many unresolved sources forming a stochastic background. A description of potential sources and the search for gravitational waves from each of these classes are detailed.
Accurate extractions of the detected gravitational wave (GW) signal waveforms are essential to validate a detection and to probe the astrophysics behind the sources producing the GWs. This however could be difficult in realistic scenarios where the signals detected by existing GW detectors could be contaminated with non-stationary and non-Gaussian noise. While the performance of existing waveform extraction methods are optimal, they are not fast enough for online application, which is important for multi-messenger astronomy. In this paper, we demonstrate that a deep learning architecture consisting of Convolutional Neural Network and bidirectional Long Short-Term Memory components can be used to extract binary black hole (BBH) GW waveforms from realistic noise in a few milli-seconds. We have tested our network systematically on injected GW signals, with component masses uniformly distributed in the range of 10 to 80 solar masses, on Gaussian noise and LIGO detector noise. We find that our model can extract GW waveforms with overlaps of more than 0.95 with pure Numerical Relativity templates for signals with signal-to-noise ratio (SNR) greater than six, and is also robust against interfering glitches. We then apply our model to all ten detected BBH events from the first (O1) and second (O2) observation runs, obtaining greater than 0.97 overlaps for all ten extracted BBH waveforms with the corresponding pure templates. We discuss the implication of our result and its future applications to GW localization and mass estimation.
Gravitational-wave signals from inspirals of binary compact objects (black holes and neutron stars) are primary targets of the ongoing searches by ground-based gravitational-wave (GW) interferometers (LIGO, Virgo, and GEO-600). We present parameter-estimation results from our Markov-chain Monte-Carlo code SPINspiral on signals from binaries with precessing spins. Two data sets are created by injecting simulated GW signals into either synthetic Gaussian noise or into LIGO detector data. We compute the 15-dimensional probability-density functions (PDFs) for both data sets, as well as for a data set containing LIGO data with a known, loud artefact (glitch). We show that the analysis of the signal in detector noise yields accuracies similar to those obtained using simulated Gaussian noise. We also find that while the Markov chains from the glitch do not converge, the PDFs would look consistent with a GW signal present in the data. While our parameter-estimation results are encouraging, further investigations into how to differentiate an actual GW signal from noise are necessary.
The LIGO Scientific Collaboration and Virgo Collaboration have carried out joint searches in LIGO and Virgo data for periodic continuous gravitational waves. These analyses range from targeted searches for gravitational-wave signals from known pulsars, for which precise ephemerides from radio or X-ray observations are used in matched filters, to all-sky searches for unknown neutron stars, including stars in binary systems. Between these extremes lie directed searches for known stars of unknown spin frequency or for new unknown sources at specific locations, such as near the galactic center or in globular clusters. Recent and ongoing searches of each type will be summarized, along with prospects for future searches using data from the Advanced LIGO and Virgo detectors.
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 which the false alarm probability is far larger than that of the LIGO-like noises. We also investigate the responses of the glitches with different frequency. We find that when the frequency of the glitches falls in that of the trained GW signals, the false alarm probability of the glitches will be much larger than that of the LIGO-like noises, and the probability of the glitches being misjudged as the GW signals may even exceed 30%.
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