Testing the normality of the gravitational wave data with a low cost recursive estimate of the kurtosis

We propose a monitoring indicator of the normality of the output of a gravitational wave detector. This indicator is based on the estimation of the kurtosis (i.e., the 4th order statistical moment normalized by the variance squared) of the data selected in a time sliding window. We show how a low cost (because recursive) implementation of such estimation is possible and we illustrate the validity of the presented approach with a few examples using simulated random noises.Comment: 4 pages, 3 figures. In the Proceedings of the 3rd workshop on Physics in Signal and Image Processing (Grenoble), 200

Data analysis challenges in transient gravitational-wave astronomy

Gravitational waves are radiative solutions of space-time dynamics predicted by Einstein's theory of General Relativity. A world-wide array of large-scale and highly sensitive interferometric detectors constantly scrutinizes the geometry of the local space-time with the hope to detect deviations that would signal an impinging gravitational wave from a remote astrophysical source. Finding the rare and weak signature of gravitational waves buried in non-stationary and non-Gaussian instrument noise is a particularly challenging problem. We will give an overview of the data-analysis techniques and associated observational results obtained so far by Virgo (in Europe) and LIGO (in the US), along with the prospects offered by the up-coming advanced versions of those detectors.Comment: 7 pages, 5 figures, Proceedings of the ARENA'12 Conference, few minor change

Adaptive filtering techniques for interferometric data preparation: removal of long-term sinusoidal signals and oscillatory transients

We propose an adaptive denoising scheme for poorly modeled non-Gaussian features in the gravitational wave interferometric data. Preliminary tests on real data show encouraging results.Comment: 4 pages, 2 figures. Proceedings of GWDAW99 (Roma, Dec. 1999), to appear in Int. J. Mod. Phys.

Uncertainty and Spectrogram Geometry

International audienceUltimate possibilities of localization for time-frequency representations are first reviewed from a joint perspective, evidencing that Heisenberg-type pointwise limits are not exclusive of sharp localization along trajectories in the plane. Spectrogram reassignment offers such a possibility and, in order to revisit its connection with uncertainty, geometrical properties of spectrograms are statistically investigated in the generic case of white Gaussian noise. Based on Voronoi tessellations and Delaunay triangulations attached to extrema, it is shown that, in a first approximation, local energy ''patches'' are distributed according to a randomized hexagonal lattice with a typical scale within a factor of a few that of minimum uncertainty Gabor logons

Making Reassignment Adjustable: the Levenberg-Marquardt Approach

accepted for publication, to appear in Proc. of IEEE Int. Conf. on Acoust., Speech and Signal Proc. ICASSP-12, Kyoto (Japan), March 25-30, 2012.International audienceThis paper presents a new time-frequency reassignment process of the spectrogram, called the Levenberg-Marquardt reassignment. Compared to the classical one, this new reassignment process uses the second-order derivatives of the phase of the short-time Fourier transform, and provides the user with a setting parameter. This parameter allows him to produce either a weaker or a stronger localization of the signal components in the time-frequency plane

Detection of gravitational-wave bursts with chirplet-like template families

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.Comment: 8 pages, 6 figures. Submitted to Class. Quantum Grav. Special issue: Proceedings of GWDAW-14, Rome (Italy), 2010; fixed several minor issue

Best chirplet chain: near-optimal detection of gravitational wave chirps

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]Comment: 23 pages, 9 figures. Accepted for publication in Phys. Rev D Some typos corrected and changes made according to referee's comment

Performance of a Chirplet-based analysis for gravitational waves from binary black hole mergers

The gravitational wave (GW) signature of a binary black hole (BBH) coalescence is characterized by rapid frequency evolution in the late inspiral and merger phases. For a system with total mass larger than 100 M_sun, ground based GW detectors are sensitive to the merger phase, and the in-band whitened waveform is a short-duration transient lasting about 10-30 ms. For a symmetric mass system with total mass between 10 and 100 M_sun, the detector is sensitive instead to the inspiral phase and the in-band signal has a longer duration, between 30 ms - 3 s. Omega is a search algorithm for GW bursts that, with the assumption of locally stationary frequency evolution, uses sine-Gaussian wavelets as a template bank to decompose interferometer strain data. The local stationarity of sine-Gaussians induces a performance loss for the detection of lower mass BBH signatures, due to the mismatch between template and signal. We present the performance of a modified version of the Omega algorithm, Chirplet Omega, which allows a linear variation of frequency, to target BBH coalescences. The use of Chirplet-like templates enhances the measured signal-to-noise ratio due to less mismatch between template and data, and increases the detectability of lower mass BBH coalescences. We present the results of a performance study of Chirplet Omega in colored Gaussian noise at initial LIGO sensitivity.Comment: 7 pages, 12 figures. Proceedings of Amaldi-9, Cardiff (UK), 201

Neural network time-series classifiers for gravitational-wave searches in single-detector periods

The search for gravitational-wave signals is limited by non-Gaussian transient noises that mimic astrophysical signals. Temporal coincidence between two or more detectors is used to mitigate contamination by these instrumental glitches. However, when a single detector is in operation, coincidence is impossible, and other strategies have to be used. We explore the possibility of using neural network classifiers and present the results obtained with three types of architectures: convolutional neural network, temporal convolutional network, and inception time. The last two architectures are specifically designed to process time-series data. The classifiers are trained on a month of data from the LIGO Livingston detector during the first observing run (O1) to identify data segments that include the signature of a binary black hole merger. Their performances are assessed and compared. We then apply trained classifiers to the remaining three months of O1 data, focusing specifically on single-detector times. The most promising candidate from our search is 2016-01-04 12:24:17 UTC. Although we are not able to constrain the significance of this event to the level conventionally followed in gravitational-wave searches, we show that the signal is compatible with the merger of two black holes with masses $m_1 = 50.7^{+10.4}_{-8.9}\,M_{\odot}$ and $m_2 = 24.4^{+20.2}_{-9.3}\,M_{\odot}$ at the luminosity distance of $d_L = 564^{+812}_{-338}\,\mathrm{Mpc}$.Comment: 29 pages, 11 figures, submitted to CQ