51 research outputs found
Image-based deep learning for classification of noise transients in gravitational wave detectors
The detection of gravitational waves has inaugurated the era of gravitational
astronomy and opened new avenues for the multimessenger study of cosmic
sources. Thanks to their sensitivity, the Advanced LIGO and Advanced Virgo
interferometers will probe a much larger volume of space and expand the
capability of discovering new gravitational wave emitters. The characterization
of these detectors is a primary task in order to recognize the main sources of
noise and optimize the sensitivity of interferometers. Glitches are transient
noise events that can impact the data quality of the interferometers and their
classification is an important task for detector characterization. Deep
learning techniques are a promising tool for the recognition and classification
of glitches. We present a classification pipeline that exploits convolutional
neural networks to classify glitches starting from their time-frequency
evolution represented as images. We evaluated the classification accuracy on
simulated glitches, showing that the proposed algorithm can automatically
classify glitches on very fast timescales and with high accuracy, thus
providing a promising tool for online detector characterization.Comment: 25 pages, 8 figures, accepted for publication in Classical and
Quantum Gravit
Classification methods for noise transients in advanced gravitational-wave detectors
Noise of non-astrophysical origin will contaminate science data taken by the
Advanced Laser Interferometer Gravitational-wave Observatory (aLIGO) and
Advanced Virgo gravitational-wave detectors. Prompt characterization of
instrumental and environmental noise transients will be critical for improving
the sensitivity of the advanced detectors in the upcoming science runs. During
the science runs of the initial gravitational-wave detectors, noise transients
were manually classified by visually examining the time-frequency scan of each
event. Here, we present three new algorithms designed for the automatic
classification of noise transients in advanced detectors. Two of these
algorithms are based on Principal Component Analysis. They are Principal
Component Analysis for Transients (PCAT), and an adaptation of LALInference
Burst (LIB). The third algorithm is a combination of an event generator called
Wavelet Detection Filter (WDF) and machine learning techniques for
classification. We test these algorithms on simulated data sets, and we show
their ability to automatically classify transients by frequency, SNR and
waveform morphology
Prospects for Stochastic Background Searches Using Virgo and LSC Interferometers
We consider the question of cross-correlation measurements using Virgo and
the LSC Interferometers (LIGO Livingston, LIGO Hanford, and GEO600) to search
for a stochastic gravitational-wave background. We find that inclusion of Virgo
into the network will substantially improve the sensitivity to correlations
above 200 Hz if all detectors are operating at their design sensitivity. This
is illustrated using a simulated isotropic stochastic background signal,
generated with an astrophysically-motivated spectrum, injected into 24 hours of
simulated noise for the LIGO and Virgo interferometers.Comment: 11 pages, uses IOP style files, submitted to CQG for GWDAW11
proceedings; revised in response to referee comment
Classification methods for noise transients in advanced gravitational-wave detectors II: performance tests on Advanced LIGO data
The data taken by the advanced LIGO and Virgo gravitational-wave detectors contains short duration noise transients that limit the significance of astrophysical detections and reduce the duty cycle of the instruments. As the advanced detectors are reaching sensitivity levels that allow for multiple detections of astrophysical gravitational-wave sources it is crucial to achieve a fast and accurate characterization of non-astrophysical transient noise shortly after it occurs in the detectors. Previously we presented three methods for the classification of transient noise sources. They are Principal Component Analysis for Transients (PCAT), Principal Component LALInference Burst (PC-LIB) and Wavelet Detection Filter with Machine Learning (WDF-ML). In this study we carry out the first performance tests of these algorithms on gravitational-wave data from the Advanced LIGO detectors. We use the data taken between the 3rd of June 2015 and the 14th of June 2015 during the 7th engineering run (ER7), and outline the improvements made to increase the performance and lower the latency of the algorithms on real data. This work provides an important test for understanding the performance of these methods on real, non stationary data in preparation for the second advanced gravitational-wave detector observation run, planned for later this year. We show that all methods can classify transients in non stationary data with a high level of accuracy and show the benefits of using multiple classifiers
LSTM and CNN application for core-collapse supernova search in gravitational wave real data
Core-collapse supernovae (CCSNe) are expected to emit
gravitational wave signals that could be detected by current and future
generation interferometers within the Milky Way and nearby galaxies. The
stochastic nature of the signal arising from CCSNe requires alternative
detection methods to matched filtering. We aim to show the potential of
machine learning (ML) for multi-label classification of different CCSNe
simulated signals and noise transients using real data. We compared the
performance of 1D and 2D convolutional neural networks (CNNs) on single and
multiple detector data. For the first time, we tested multi-label
classification also with long short-term memory (LSTM) networks. We
applied a search and classification procedure for CCSNe signals, using an event
trigger generator, the Wavelet Detection Filter (WDF), coupled with ML. We used
time series and time-frequency representations of the data as inputs to the ML
models. To compute classification accuracies, we simultaneously injected, at
detectable distance of 1\,kpc, CCSN waveforms, obtained from recent
hydrodynamical simulations of neutrino-driven core-collapse, onto
interferometer noise from the O2 LIGO and Virgo science run. We
compared the performance of the three models on single detector data. We then
merged the output of the models for single detector classification of noise and
astrophysical transients, obtaining overall accuracies for LIGO ()
and () for Virgo. We extended our analysis to the multi-detector case
using triggers coincident among the three ITFs and achieved an accuracy of
.Comment: 10 pages, 13 figures. Accepted by A&A journa
On line power spectra identification and whitening for the noise in interferometric gravitational wave detectors
In this paper we address both to the problem of identifying the noise Power
Spectral Density of interferometric detectors by parametric techniques and to
the problem of the whitening procedure of the sequence of data. We will
concentrate the study on a Power Spectral Density like the one of the
Italian-French detector VIRGO and we show that with a reasonable finite number
of parameters we succeed in modeling a spectrum like the theoretical one of
VIRGO, reproducing all its features. We propose also the use of adaptive
techniques to identify and to whiten on line the data of interferometric
detectors. We analyze the behavior of the adaptive techniques in the field of
stochastic gradient and in the
Least Squares ones.Comment: 28 pages, 21 figures, uses iopart.cls accepted for pubblication on
Classical and Quantum Gravit
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