Elena Cuoco

The Virgo interferometer

The Virgo gravitational wave detector is an interferometer with 3 km long arms in construction near Pisa to be commissioned in the year 2000. Virgo has been designed to achieve a strain sensitivity of a few times at 200 Hz. A large effort has gone into the conception of the mirror suspension system, which is expected to reduce noise to the level of at 10 Hz. The expected signals and main sources of noise are briefly discussed; the choices made are illustrated together with the present status of the experiment.

Measurement of the VIRGO superattenuator performance for seismic noise suppression

Below a few tens of hertz interferometric detection of gravitational waves is masked by seismic vibrations of the optical components. In order to isolate the mirrors of the VIRGO interferometer, a sophisticated suspension system, called superattenuator, has been developed. Its working principle is based on a multistage pendulum acting on seismic vibrations as a chain of second order mechanical low-pass filters. A complete superattenuator has been built and tested. This apparatus allows extending the VIRGO detection band down to a few Hz. A detailed description of the attenuation system and its performance are presented in this article.

An inverted pendulum preisolator stage for the VIRGO suspension system

The design of a new preisolator stage for the VIRGO superattenuator is presented. The device is essentially a 6 m high inverted pendulum with horizontal resonant frequency of 30 mHz. An isolation of 65 dB at 1 Hz has been achieved. Very low forces are needed to move the whole superattenuator acting on the inverted pendulum. For this reason, the system is a suitable platform for the active control of the mirror suspension.

Inertial control of the mirror suspensions of the VIRGO interferometer for gravitational wave detection

In order to achieve full detection sensitivity at low frequencies, the mirrors of interferometric gravitational wave detectors must be isolated from seismic noise. The VIRGO vibration isolator, called the superattenuator, is fully effective at frequencies above 4 Hz. But the residual motion of the mirror at the mechanical resonant frequencies of the system is too large for the interferometer locking system and must be damped. A multidimensional feedback system, using inertial sensors and digital processing, has been designed for this purpose. An experimental procedure for determining the feedback control of the system has been defined. In this article a full description of the system is given and experimental results are presented.

Relevance of Newtonian seismic noise for the VIRGO interferometer sensitivity

In this paper we analyse the noise level induced by changes in the mass density distribution around the Virgo interferometric antenna. These stochastic mass density fluctuations generate a gravitational field which couples directly to the mirrors of the optical apparatus, and it could be relevant if the planned final sensitivity of the Virgo interferometer is to be reached.

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.

On-line power spectra identification and whitening for the noise in interferometric gravitational wave detectors

The knowledge of the noise power spectral density of an interferometric detector of gravitational waves is fundamental for detection algorithms and for the analysis of the data. In this paper we address both the problem of identifying the noise power spectral density of interferometric detectors by parametric techniques and the problem of the whitening procedure of the sequence of data. We will concentrate the study on a power spectral density like that of the Italian–French detector VIRGO and we show that with a reasonable number of parameters we succeed in modelling a spectrum like the theoretical one of VIRGO, reproducing all of its features. We also propose the use of adaptive techniques to identify and to whiten the data of interferometric detectors on-line. We analyse the behaviour of the adaptive techniques in the field of stochastic gradient and in the least-squares filters. As a result, we find that the least-squares lattice filter is the best among those we have analysed. It succeeds optimally in following all the peaks of the noise power spectrum, and one of its outputs is the whitened part of the spectrum. Besides, the fast convergence of this algorithm, it lets us follow the slow non-stationarity of the noise. These procedures could be used to whiten the overall power spectrum or only some region of it. The advantage of the techniques we propose is that they do not require a priori knowledge of the noise power spectrum to be analysed. Moreover, the adaptive techniques let us identify and remove the spectral line, without building any physical model of the source that produced it.

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.

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 spectral shape, injected into 24 h of simulated noise for the LIGO and Virgo interferometers.

Noise parametric identification and whitening for LIGO 40-m interferometer data

One of the goals of gravitational data wave analysis is the knowledge and accurate estimation of the noise power spectral density of the data taken by the detector, this being necessary in the detection algorithms. In this paper we show how it is possible to estimate the noise power spectral density of gravitational wave detectors using modern parametric techniques and how it is possible to whiten the noise data before they pass to the algorithms for gravitational wave detection. We report the analysis we made of data taken by the Caltech 40-m prototype interferometer to identify the noise power spectral density and to whiten the sequence of noise. We concentrate our study on data taken in November 1994; in particular, we analyze two frames of data: the 18nov94.2.frame and the 19nov94.2.frame. We show that it is possible to whiten these data, to a good degree of whiteness, using a high order whitening filter. Moreover, we can choose to whiten only a restricted band of frequencies around the region we are interested in, obtaining a higher level of whiteness.