Multiresolution techniques for the detection of gravitational-wave bursts

Chatterji, S.; Blackburn, L.; Martín, G.; Katsavounidis, E.

doi:10.1088/0264-9381/21/20/024

Cited by 215 publications

(276 citation statements)

References 18 publications

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“…Consequently, to search for and characterize these signals, one must presently use more general morphology-independent search techniques. Here, transient bursts of GWs can be identified in the detector output data as excess power localized in the time-frequency domain (see e.g., [38][39][40][41]) and the impinging GW waveform can be reconstructed by considering the coherent signal energy coincident in multiple detectors [42][43][44] or by projecting the data onto bases formed from representative catalogues of simulations of the un-modelled signal [45,46]. Additionally, in the case of the highfrequency GW burst following a binary neutron star coalescence, it is reasonable to assume that the time of coalescence will be known to high accuracy from the inspiral portion of the signal [47,48], thus increasing the detection confidence for the post-merger part.…”

Section: Introductionmentioning

confidence: 99%

Prospects for high frequency burst searches following binary neutron star coalescence with advanced gravitational wave detectors

et al. 2014

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The equation of state plays a critical role in the physics of the merger of two neutron stars. Recent numerical simulations with microphysical equation of state suggest the outcome of such events depends on the mass of the neutron stars. For less massive systems, simulations favor the formation of a hypermassive, quasi-stable neutron star, whose oscillations produce a short, high frequency burst of gravitational radiation. Its dominant frequency content is tightly correlated with the radius of the neutron star, and its measurement can be used to constrain the supranuclear equation of state. In contrast, the merger of higher mass systems results in prompt gravitational collapse to a black hole. We have developed an algorithm which combines waveform reconstruction from a morphologyindependent search for gravitational wave transients with Bayesian model selection, to discriminate between post-merger scenarios and accurately measure the dominant oscillation frequency. We demonstrate the efficacy of the method using a catalogue of simulated binary merger signals in data from LIGO and Virgo, and we discuss the prospects for this analysis in advanced ground-based gravitational wave detectors. From the waveforms considered in this work and assuming an optimally oriented source, we find that the post-merger neutron star signal may be detectable by this technique to ∼ 10-25 Mpc. We also find that we successfully discriminate between the post-merger scenarios with ∼ 95% accuracy and determine the dominant oscillation frequency of surviving post-merger neutron stars to within ∼ 10 Hz, averaged over all detected signals. This leads to an uncertainty in the estimated radius of a non-rotating 1.6 M⊙ reference neutron star of ∼ 100 m.

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Section: Introductionmentioning

confidence: 99%

Prospects for high frequency burst searches following binary neutron star coalescence with advanced gravitational wave detectors

et al. 2014

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“…Techniques for detection of burst-like triggers in the GW instrumental output data stream are described con-cisely in an earlier paper [15]. In general, such techniques project the data onto a basis that spans the parameter space of the burst-like signals.…”

Section: Techniques For Detecting Triggers In Ligo Datamentioning

confidence: 99%

“…The MHC pipeline implements a hierarchical algorithm [14][15][16][17][18][19] that applies a variance minimization criterion and groups together burst triggers detected by the KW [11] pipeline based on their similarity in the higher dimensional space spanned by properties like the trigger duration, frequency, snr and statistical significance.…”

Section: Multidimensional Hierarchical Classification Algorithmmentioning

confidence: 99%

“…We have used triggers found in the Omega trigger catalog [15,29,49] during S6. Three hundred and forty Omega triggers from the GW channel seen in the test data set (with snr > 12) are selected and subjected to the analysis pipeline.…”

Section: Ligo Sixth Science Run Trigger Databasementioning

confidence: 99%

“…kleine welle (KW) [15], omega (OP) [29] and waveburst (WB) [20]. The KW pipeline works on multiple channels -the GW channel and several hundreds of auxiliary and environmental channels.…”

Section: Introductionmentioning

confidence: 99%

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New approach to time domain classification of broadband noise in gravitational wave data

Mukherjee¹,

Rizwan²,

Biswas³

2012

Phys. Rev. D

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Transient broadband noise in gravitational wave (GW) detectors, also known as noise triggers (referred to as triggers for brevity), can often be a deterrant to the efficiency with which astrophysical search pipelines detect sources. It is important to understand their instrumental or environmental origin so that they could be eliminated or accounted for in the data. Since the number of triggers is large, data mining approaches such as clustering and classification are useful tools for this task. Classification of triggers based on a handful of discrete properties has been done in the past. A rich information content is available in the waveform or 'shape' of the triggers that has had a rather restricted exploration so far. This paper presents a new way to classify triggers deriving information from both trigger waveforms as well as their discrete physical properties using a sequential combination of the Longest Common Sub-Sequence (LCSS) and LCSS coupled with Fast Time Series Evaluation (FTSE) for waveform classification and the multidimensional hierarchical classification (MHC) analysis for the grouping based on physical properties. A generalized k-means algorithm is used with the LCSS (and LCSS+FTSE) for clustering the triggers using a validity measure to determine the correct number of clusters in absence of any prior knowledge. The results have been demonstrated by simulations and by application to a segment of real LIGO data from the sixth science run.

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Training Process of Unsupervised Learning Architecture for Gravity Spy Dataset

et al. 2022

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Transient noise appearing in the data from gravitational-wave detectors frequently causes problems, such as instability of the detectors and overlapping or mimicking gravitational-wave signals. Because transient noise is considered to be associated with the environment and instrument, its classification would help to understand its origin and improve the detector's performance. In a previous study, an architecture for classifying transient noise using a time-frequency 2D image (spectrogram) is proposed, which uses unsupervised deep learning combined with variational autoencoder and invariant information clustering. The proposed unsupervised-learning architecture is applied to the Gravity Spy dataset, which consists of Advanced Laser Interferometer Gravitational-Wave Observatory (Advanced LIGO) transient noises with their associated metadata to discuss the potential for online or offline data analysis. In this study, focused on the Gravity Spy dataset, the training process of unsupervised-learning architecture of the previous study is examined and reported.

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Multiresolution techniques for the detection of gravitational-wave bursts

Cited by 215 publications

References 18 publications

Prospects for high frequency burst searches following binary neutron star coalescence with advanced gravitational wave detectors

Prospects for high frequency burst searches following binary neutron star coalescence with advanced gravitational wave detectors

New approach to time domain classification of broadband noise in gravitational wave data

Training Process of Unsupervised Learning Architecture for Gravity Spy Dataset

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