1,126 research outputs found
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 and at the luminosity distance of .Comment: 29 pages, 11 figures, submitted to CQ
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