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Event distribution of injected signals (violet) and reconstructed by cWB (green) for mass-ratio (left panel) and chirp-mass (right panel). cWB pipeline reconstructed ∼6.5×104 out of about 1.7×105 signals, injected according to table 2.
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The quest to observe gravitational waves challenges our ability to discriminate signals from detector noise. This issue is especially relevant for transient gravitational waves searches with a robust eyes wide open approach, the so called all- sky burst searches. Here we show how signal classification methods inspired by broad astrophysical charact...
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We employ the formalism developed in [1] and [2] to study the prospect of detecting an anisotropic Stochastic Gravitational Wave Background (SGWB) with the Laser Interferometer Space Antenna (LISA) alone, and combined with the proposed space-based interferometer Taiji. Previous analyses have been performed in the frequency domain only. Here, we stu...
Citations
... Similar to the standard veto procedure, an efficient reduction of the cWB false-alarm rate can be also achieved with the supervised classification algorithms. In [89] a neural network method analyzing the time-frequency patterns of reconstructed cWB triggers was suggested to improve the detection of BBH signals. In [90] a machine-learning method was used to improve the identification of CCSNe. ...
This paper presents a search for generic short-duration gravitational-wave (GW) transients (or GW bursts) in the data from the third observing run of Advanced LIGO and Advanced Virgo. We use coherent WaveBurst (cWB) pipeline enhanced with a decision-tree classification algorithm for more efficient separation of GW signals from noise transients. The machine-learning (ML) algorithm is trained on a representative set of the noise events and a set of simulated stochastic signals that are not correlated with any known signal model. This training procedure preserves the model-independent nature of the search. We demonstrate that the ML-enhanced cWB pipeline can detect GW signals at a larger distance than the previous model-independent searches, and the sensitivity improvements are achieved across a broad spectrum of simulated signals used in the analysis. At a false-alarm rate of one event per century, the detectable signal amplitudes are reduced up to almost an order of magnitude, most notably for cosmic strings. By testing the pipeline for the detection of compact binaries, we verified that it detects more systems in a wide range of masses from stellar mass to intermediate-mass black-holes, both with circular and elliptical orbits. After excluding previously detected compact binaries, no new gravitational-wave signals are observed for the two-fold Hanford-Livingston and the three-fold Hanford-Livingston-Virgo detector networks. With the improved sensitivity of the all-sky search, we obtain the most stringent constraints on the isotropic emission of gravitational-wave energy from the short-duration burst sources.
... The cWB algorithm has been recently used in combination with machine learning (ML) algorithms for various studies [21][22][23][24]. In this paper, the standard cWB pipeline sensitivity to BBH mergers is enhanced by using the ML method as described in Ref. [25]. ...
In this work, we use the coherent WaveBurst (cWB) pipeline enhanced with machine learning (ML) to search for binary black hole (BBH) mergers in the Advanced LIGO-Virgo strain data from the third observing run (O3). We detect, with equivalent or higher significance, all gravitational-wave (GW) events previously reported by the standard cWB search for BBH mergers in the third GW Transient Catalog (GWTC-3). The ML-enhanced cWB search identifies five additional GW candidate events from the catalog that were previously missed by the standard cWB search. Moreover, we identify three marginal candidate events not listed in GWTC-3. For simulated events distributed uniformly in a fiducial volume, we improve the detection efficiency with respect to the standard cWB search by approximately for both stellar-mass and intermediate mass black hole binary mergers, detected with a false-alarm rate less than . We show the robustness of the ML-enhanced search for detection of generic BBH signals by reporting increased sensitivity to the spin-precessing and eccentric BBH events as compared to the standard cWB search. Furthermore, we compare the improvement of the ML-enhanced cWB search for different detector networks.
... Machine learning (ML) techniques are the possible option to address such problems as they offer powerful tools for classification between signal and noisy transients [24][25][26][27][28][29][30][31][32][33]. More generically, ML ideas have notable potential for improving the detection sensitivity of unmodeled GW signals [1,[34][35][36][37]. ...
Coherent WaveBurst is a generic, multi-detector gravitational wave burst search based on the excess power approach. The coherent WaveBurst algorithm currently employed in the all-sky short-duration gravitational wave burst search uses a conditional approach on a selected attributes in the multi-dimensional event attribute space to distinguish between noisy event from that of astrophysical origin. We have been developing a supervised machine learning approach based on the Gaussian mixture modeling to model the attribute space for signals as well as noise events to enhance the probability of burst detection. We further extend the GMM approach to the all-sky short-duration coherent WaveBurst search as a post-processing step on events from the first half of the third observing run (O3a). We show an improvement in sensitivity to generic gravitational wave burst signal morphologies as well as the astrophysical source such as core-collapse supernova models due to the application of our Gaussian mixture model approach to coherent WaveBurst triggers. The Gaussian mixture model method recovers the gravitational wave signals from massive compact binary coalescences identified by coherent WaveBurst targeted for binary black holes in GWTC-2, with better significance than the all-sky coherent WaveBurst search. No additional significant gravitational wave bursts are observed.
... However, there have been ML searches for specific burst source types. In reference [164], the authors employ a neural network algorithm to reduce the impact of glitches on the cWB burst search and increase the significance of the CBC signals which are detected by the pipeline. ...
Machine learning has emerged as a popular and powerful approach for solving problems in astrophysics. We review applications of machine learning techniques for the analysis of ground-based gravitational-wave (GW) detector data. Examples include techniques for improving the sensitivity of Advanced Laser Interferometer GW Observatory and Advanced Virgo GW searches, methods for fast measurements of the astrophysical parameters of GW sources, and algorithms for reduction and characterization of non-astrophysical detector noise. These applications demonstrate how machine learning techniques may be harnessed to enhance the science that is possible with current and future GW detectors.
... More recently it was used for the first time on simulated continuous gravitational wave signals [29] and it was applied to the related long transient signals [30]. Furthermore DNNs have been studied as a follow-up method for CW searches [31,32], as well as for parameter estimation of searches for compact binary merger signals [33,34] and for a multitude of other gravitational-wave-search related applications such as classifying disturbances (glitches) and searches for unmodeled burst signals [35][36][37][38][39][40][41]. ...
The sensitivity of wide-parameter-space searches for continuous gravitational waves is limited by computational cost. Recently it was shown that deep neural networks (DNNs) can perform all-sky searches directly on (single-detector) strain data [C. Dreissigacker , Phys. Rev. D 100, 044009 (2019)], potentially providing a low-computing-cost search method that could lead to a better overall sensitivity. Here we expand on this study in two respects: (i) using (simulated) strain data from two detectors simultaneously, and (ii) training for directed (i.e., single sky-position) searches in addition to all-sky searches. For a data time span of T=105 s, the all-sky two-detector DNN is about 7% less sensitive (in amplitude h0) at low frequency (f=20 Hz), and about 51% less sensitive at high frequency (f=1000 Hz) compared to fully-coherent matched-filtering (using weave). In the directed case the sensitivity gap compared to matched-filtering ranges from about 7%–14% at f=20 Hz to about 37%–49% at f=1500 Hz. Furthermore we assess the DNN’s ability to generalize in signal frequency, spin down and sky-position, and we test its robustness to realistic data conditions, namely gaps in the data and using real LIGO detector noise. We find that the DNN performance is not adversely affected by gaps in the test data or by using a relatively undisturbed band of LIGO detector data instead of Gaussian noise. However, when using a more disturbed LIGO band for the tests, the DNN’s detection performance is substantially degraded due to the increase in false alarms, as expected.
... More recently it was used for the first time on simulated continuous gravitational wave signals [1] and it was applied to the related long transient signals [27]. Furthermore DNNs have been studied as a followup method for CW searches [28,29], as well as for parameter estimation of searches for compact binary merger signals [30,31] and for a multitude of other gravitationalwave-search related applications such as classifying disturbances (glitches) and searches for unmodeled burst signals [32][33][34][35][36][37][38]. ...
The sensitivity of wide-parameter-space searches for continuous gravitational waves is limited by computational cost. Recently it was shown that Deep Neural Networks (DNNs) can perform all-sky searches directly on (single-detector) strain data, potentially providing a low-computing-cost search method that could lead to a better overall sensitivity. Here we expand on this study in two respects: (i) using (simulated) strain data from two detectors simultaneously, and (ii) training for directed (i.e.\ single sky-position) searches in addition to all-sky searches. For a data timespan of , the all-sky two-detector DNN is about less sensitive (in amplitude ) at low frequency (), and about less sensitive at high frequency () compared to fully-coherent matched-filtering (using WEAVE). In the directed case the sensitivity gap compared to matched-filtering ranges from about at to about at . Furthermore we assess the DNN's ability to generalize in signal frequency, spindown and sky-position, and we test its robustness to realistic data conditions, namely gaps in the data and using real LIGO detector noise. We find that the DNN performance is not adversely affected by gaps in the test data or by using a relatively undisturbed band of LIGO detector data instead of Gaussian noise. However, when using a more disturbed LIGO band for the tests, the DNN's detection performance is substantially degraded due to the increase in false alarms, as expected.
... More recently DNNs have started to draw attention in the field of gravitational-wave searches (i) as a classifier for non-Gaussian detector transients (glitches) [24][25][26][27], (ii) as a search method for unmodeled burst signals [28,29] in time-frequency images produced by coherent WaveBurst [30], and (iii) as a direct detection method for black-hole merger signals in gravitational-wave strain data [31][32][33][34][35][36]. ...
We present a first proof-of-principle study for using deep neural networks (DNNs) as a novel search method for continuous gravitational waves (CWs) from unknown spinning neutron stars. The sensitivity of current wide-parameter-space CW searches is limited by the available computing power, which makes neural networks an interesting alternative to investigate, as they are extremely fast once trained and have recently been shown to rival the sensitivity of matched filtering for black-hole merger signals [D. George and E. A. Huerta, Phys. Rev. D 97, 044039 (2018); H. Gabbard, M. Williams, F. Hayes, and C. Messenger, Phys. Rev. Lett. 120, 141103 (2018)]. We train a convolutional neural network with residual (shortcut) connections and compare its detection power to that of a fully coherent matched-filtering search using the Weave pipeline [K. Wette, S. Walsh, R. Prix, and M. A. Papa, Phys. Rev. D 97, 123016 (2018)]. As test benchmarks we consider two types of all-sky searches over the frequency range from 20 to 1000 Hz: an “easy” search using T=105 s of data, and a “harder” search using T=106 s. The detection probability pdet is measured on a signal population for which matched filtering achieves pdet=90% in Gaussian noise. In the easiest test case (T=105 s at 20 Hz) the DNN achieves pdet∼88%, corresponding to a loss in sensitivity depth of ∼5% versus coherent matched filtering. However, at higher frequencies and for longer observation times the DNN detection power decreases, until pdet∼13% and a loss of ∼66% in sensitivity depth in the hardest case (T=106 s at 1000 Hz). We study the DNN generalization ability by testing on signals of different frequencies, spindowns and signal strengths than they were trained on. We observe excellent generalization: only five networks, each trained at a different frequency, would be able to cover the whole frequency range of the search.
... This reduces the feasibility of matchedfiltering. Generic analyses that target signals of unknown morphology might be less efficient than matched-filtering, but they have been shown to be able to extract the main features of post-merger signals, such as its main frequency components [220,221,494,495,223,496,363]. Below, more details on these works are given. ...
In this article, I introduce ideas and techniques to extract information about the equation of state of matter at very high densities from gravitational waves emitted before, during and after the merger of binary neutron stars. I also review current work and results on the actual use of the first gravitational-wave observation of a neutron-star merger to set constraints on properties of such equation of state. In passing, I also touch on the possibility that what we observe in gravitational waves are not neutron stars, but something more exotic. In order to make this article more accessible, I also review the dynamics and gravitational-wave emission of neutron-star mergers in general, with focus on numerical simulations and on which representations of the equation of state are used for studies on binary systems.
... More recently DNNs have started to draw attention in the field of gravitational-wave searches: (i) as a classifier for non-Gaussian detector transients (glitches) [26][27][28][29], (ii) as a search method for unmodelled burst signals [30,31] in time-frequency images produced by coherent WaveBurst [32], and (iii) as a direct detection method for black-hole merger signals in gravitational-wave strain data [1,2,[33][34][35][36]. ...
We present a first proof-of-principle study for using deep neural networks (DNNs) as a novel search method for continuous gravitational waves (CWs) from unknown spinning neutron stars. The sensitivity of current wide-parameter-space CW searches is limited by the available computing power, which makes neural networks an interesting alternative to investigate, as they are extremely fast once trained and have recently been shown to rival the sensitivity of matched filtering for black-hole merger signals. We train a convolutional neural network with residual (short-cut) connections and compare its detection power to that of a fully-coherent matched-filtering search using the WEAVE pipeline. As test benchmarks we consider two types of all-sky searches over the frequency range from to : an `easy' search using of data, and a `harder' search using . Detection probability is measured on a signal population for which matched filtering achieves in Gaussian noise. In the easiest test case ( at ) the DNN achieves , corresponding to a loss in sensitivity depth of versus coherent matched filtering. However, at higher-frequencies and longer observation time the DNN detection power decreases, until and a loss of in sensitivity depth in the hardest case ( at ). We study the DNN generalization ability by testing on signals of different frequencies, spindowns and signal strengths than they were trained on. We observe excellent generalization: only five networks, each trained at a different frequency, would be able to cover the whole frequency range of the search.
This paper presents a search for generic short-duration gravitational-wave (GW) transients (or GW bursts) in the data from the third observing run of Advanced LIGO and Advanced Virgo. We use a coherent WaveBurst (cWB) pipeline enhanced with a decision-tree classification algorithm for more efficient separation of GW signals from noise transients. The machine-learning (ML) algorithm is trained on a representative set of noise events and a set of simulated stochastic signals that are not correlated with any known signal model. This training procedure preserves the model-independent nature of the search. We demonstrate that the ML-enhanced cWB pipeline can detect GW signals at a larger distance than previous model-independent searches. The sensitivity improvements are achieved across the broad spectrum of simulated signals, with the goal of testing the robustness of this model-agnostic search. At a false-alarm rate of one event per century, the detectable signal amplitudes are reduced up to almost an order of magnitude, most notably for the single-cycle signal morphologies. This ML-enhanced pipeline also improves the detection efficiency of compact binary mergers in a wide range of masses, from stellar mass to intermediate-mass black holes, both with circular and elliptical orbits. After excluding previously detected compact binaries, no new gravitational-wave signals are observed for the twofold Hanford-Livingston and the threefold Hanford-Livingston-Virgo detector networks. With the improved sensitivity of the all-sky search, we obtain the most stringent constraints on the isotropic emission of gravitational-wave energy from short-duration burst sources.
