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Register a new userInstrumental vetoes for transient gravitational-wave triggers using noise-coupling models: The bilinear-coupling veto
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LIGO and Virgo recently completed searches for gravitational waves at their initial target sensitivities, and soon Advanced LIGO and Advanced Virgo will commence observations with even better capabilities. In the search for short duration signals, such as coalescing compact binary inspirals or burst events, noise transients can be problematic. Interferometric gravitational-wave detectors are highly complex instruments, and, based on the experience from the past, the data often contain a large number of noise transients that are not easily distinguishable from possible gravitational-wave signals. In order to perform a sensitive search for short-duration gravitational-wave signals it is important to identify these noise artifacts, and to veto them. Here we describe such a veto, the bilinear-coupling veto, that makes use of an empirical model of the coupling of instrumental noise to the output strain channel of the interferometric gravitational-wave detector. In this method, we check whether the data from the output strain channel at the time of an apparent signal is consistent with the data from a bilinear combination of auxiliary channels. We discuss the results of the application of this veto on recent LIGO data, and its possible utility when used with data from Advanced LIGO and Advanced Virgo.
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In the coming years gravitational-wave detectors will undergo a series of improvements, with an increase in their detection rate by about an order of magnitude. Routine detections of gravitational-wave signals promote novel astrophysical and fundamental theory studies, while simultaneously leading to an increase in the number of detections temporally overlapping with instrumentally- or environmentally-induced transients in the detectors (glitches), often of unknown origin. Indeed, this was the case for the very first detection by the LIGO and Virgo detectors of a gravitational-wave signal consistent with a binary neutron star coalescence, GW170817. A loud glitch in the LIGO-Livingston detector, about one second before the merger, hampered coincident detection (which was initially achieved solely with LIGO-Hanford data). Moreover, accurate source characterization depends on specific assumptions about the behavior of the detector noise that are rendered invalid by the presence of glitches. In this paper, we present the various techniques employed for the initial mitigation of the glitch to perform source characterization of GW170817 and study advantages and disadvantages of each mitigation method. We show that, despite the presence of instrumental noise transients louder than the one affecting GW170817, we are still able to produce unbiased measurements of the intrinsic parameters from simulated injections with properties similar to GW170817.
We present a new veto procedure to distinguish between continuous gravitational wave (CW) signals and the detector artifacts that can mimic their behavior. The veto procedure exploits the fact that a long-lasting coherent disturbance is less likely than a real signal to exhibit a Doppler modulation of astrophysical origin. Therefore, in the presence of an outlier from a search, we perform a multi-step search around the frequency of the outlier with the Doppler modulation turned off (DM-off), and compare these results with the results from the original (DM-on) search. If the results from the DM-off search are more significant than those from the DM-on search, the outlier is most likely due to an artifact rather than a signal. We tune the veto procedure so that it has a very low false dismissal rate. With this veto, we are able to identify as coherent disturbances >99.9% of the 6349 candidates from the recent all-sky low-frequency Einstein@Home search on the data from the Advanced LIGO O1 observing run [1]. We present the details of each identified disturbance in the Appendix.
Identifying the presence of a gravitational wave transient buried in non-stationary, non-Gaussian noise which can often contain spurious noise transients (glitches) is a very challenging task. For a given data set, transient gravitational wave searches produce a corresponding list of triggers that indicate the possible presence of a gravitational wave signal. These triggers are often the result of glitches mimicking gravitational wave signal characteristics. To distinguish glitches from genuine gravitational wave signals, search algorithms estimate a range of trigger attributes, with thresholds applied to these trigger properties to separate signal from noise. Here, we present the use of Gaussian mixture models, a supervised machine learning approach, as a means of modelling the multi-dimensional trigger attribute space. We demonstrate this approach by applying it to triggers from the coherent Waveburst search for generic bursts in LIGO O1 data. By building Gaussian mixture models for the signal and background noise attribute spaces, we show that we can significantly improve the sensitivity of the coherent Waveburst search and strongly suppress the impact of glitches and background noise, without the use of multiple search bins as employed by the original O1 search. We show that the detection probability is enhanced by a factor of 10, leading enhanced statistical significance for gravitational wave signals such as GW150914.
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%.
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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