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Register a new userDetection of gravitational-wave bursts with chirplet-like template families
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Added by
Eric Chassande-Mottin
Publication date
2010
fields
Physics
and research's language is
English
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Gravitational Wave (GW) burst detection algorithms typically rely on the hypothesis that the burst signal is locally stationary, that is it changes slowly with frequency. Under this assumption, the signal can be decomposed into a small number of wavelets with constant frequency. This justifies the use of a family of sine-Gaussian templates in the Omega pipeline, one of the algorithms used in LIGO-Virgo burst searches. However there are plausible scenarios where the burst frequency evolves rapidly, such as in the merger phase of a binary black hole and/or neutron star coalescence. In those cases, the local stationarity of sine-Gaussians induces performance losses, due to the mismatch between the template and the actual signal. We propose an extension of the Omega pipeline based on chirplet-like templates. Chirplets incorporate an additional parameter, the chirp rate, to control the frequency variation. In this paper, we show that the Omega pipeline can easily be extended to include a chirplet template bank. We illustrate the method on a simulated data set, with a family of phenomenological binary black-hole coalescence waveforms embedded into Gaussian LIGO/Virgo-like noise. Chirplet-like templates result in an enhancement of the measured signal-to-noise ratio.
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The list of putative sources of gravitational waves possibly detected by the ongoing worldwide network of large scale interferometers has been continuously growing in the last years. For some of them, the detection is made difficult by the lack of a complete information about the expected signal. We concentrate on the case where the expected GW is a quasi-periodic frequency modulated signal i.e., a chirp. In this article, we address the question of detecting an a priori unknown GW chirp. We introduce a general chirp model and claim that it includes all physically realistic GW chirps. We produce a finite grid of template waveforms which samples the resulting set of possible chirps. If we follow the classical approach (used for the detection of inspiralling binary chirps, for instance), we would build a bank of quadrature matched filters comparing the data to each of the templates of this grid. The detection would then be achieved by thresholding the output, the maximum giving the individual which best fits the data. In the present case, this exhaustive search is not tractable because of the very large number of templates in the grid. We show that the exhaustive search can be reformulated (using approximations) as a pattern search in the time-frequency plane. This motivates an approximate but feasible alternative solution which is clearly linked to the optimal one. [abridged version of the abstract]
Gravitational wave (GW) detections have enriched our understanding of the universe. To date, all single-source GW events were found by interferometer-type detectors. We study a detection method using astrometric solutions from photometric surveys and demonstrate that it offers a highly flexible frequency range, uniquely complementing existing detection methods. From repeated point-source astrometric measurements, we may extract GW-induced deflections and infer wave parameters. This method can be applied to any photometric surveys measuring relative astrometry. We show that high-cadence observations of the galactic bulge, such as offered by the Roman Space Telescopes Exoplanet MicroLensing (EML) survey, can be a potent GW probe with complementary frequency range to Gaia, pulsar timing arrays (PTAs), and the Laser Interferometer Space Antenna (LISA). We calculate that the Roman EML survey is sensitive to GWs with frequencies ranging from $7.7times10^{-8}$Hz to $5.6times10^{-4}$Hz, which opens up a unique GW observing window for supermassive black hole binaries and their waveform evolution. While the detection threshold assuming the currently expected performance proves too high for detecting individual GWs in light of the expected supermassive black hole binary population distribution, we show that binaries with chirp mass $M_c>10^{8.3}~M_odot$ out to 100 Mpc can be detected if the telescope is able to achieve an astrometric accuracy of 0.11 mas. To confidently detect binaries with $M_c>10^{7}~M_odot$ out to 50 Mpc, a factor of 100 sensitivity improvement is required. We propose several improvement strategies, including recovering the mean astrometric deflection and increasing astrometric accuracy, number of observed stars, field-of-view size, and observational cadence. We discuss how other existing and planned photometric surveys could contribute to detecting GWs via astrometry.
With the advanced LIGO and Virgo detectors taking observations the detection of gravitational waves is expected within the next few years. Extracting astrophysical information from gravitational wave detections is a well-posed problem and thoroughly studied when detailed models for the waveforms are available. However, one motivation for the field of gravitational wave astronomy is the potential for new discoveries. Recognizing and characterizing unanticipated signals requires data analysis techniques which do not depend on theoretical predictions for the gravitational waveform. Past searches for short-duration un-modeled gravitational wave signals have been hampered by transient noise artifacts, or glitches, in the detectors. In some cases, even high signal-to-noise simulated astrophysical signals have proven difficult to distinguish from glitches, so that essentially any plausible signal could be detected with at most 2-3 $sigma$ level confidence. We have put forth the BayesWave algorithm to differentiate between generic gravitational wave transients and glitches, and to provide robust waveform reconstruction and characterization of the astrophysical signals. Here we study BayesWaves capabilities for rejecting glitches while assigning high confidence to detection candidates through analytic approximations to the Bayesian evidence. Analytic results are tested with numerical experiments by adding simulated gravitational wave transient signals to LIGO data collected between 2009 and 2010 and found to be in good agreement.
General Relativity predicts only two tensor polarization modes for gravitational waves while at most six possible polarization modes of gravitational waves are allowed in the general metric theory of gravity. The number of polarization modes is totally determined by the specific modified theory of gravity. Therefore, the determination of polarization modes can be used to test gravitational theory. We introduce a concrete data analysis pipeline for a single space-based detector such as LISA to detect the polarization modes of gravitational waves. Apart from being able to detect mixtures of tensor and extra polarization modes, this method also has the added advantage that no waveform model is needed and monochromatic gravitational waves emitted from any compact binary system with known sky position and frequency can be used. We apply the data analysis pipeline to the reference source J0806.3+1527 of TianQin with 90-days simulation data, and we show that $alpha$ viewed as an indicative of the intrinsic strength of the extra polarization component relative to the tensor modes can be limited below 0.5 for LISA and below 0.2 for Taiji. We investigate the possibility to detect the nontensorial polarization modes with the combined network of LISA, TianQin and Taiji and find that $alpha$ can be limited below 0.2.
We present an application of anomaly detection techniques based on deep recurrent autoencoders to the problem of detecting gravitational wave signals in laser interferometers. Trained on noise data, this class of algorithms could detect signals using an unsupervised strategy, i.e., without targeting a specific kind of source. We develop a custom architecture to analyze the data from two interferometers. We compare the obtained performance to that obtained with other autoencoder architectures and with a convolutional classifier. The unsupervised nature of the proposed strategy comes with a cost in terms of accuracy, when compared to more traditional supervised techniques. On the other hand, there is a qualitative gain in generalizing the experimental sensitivity beyond the ensemble of pre-computed signal templates. The recurrent autoencoder outperforms other autoencoders based on different architectures. The class of recurrent autoencoders presented in this paper could complement the search strategy employed for gravitational wave detection and extend the reach of the ongoing detection campaigns.
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