All-sky search for periodic gravitational waves in LIGO S4 data

We report on an all-sky search with the LIGO detectors for periodic gravitational waves in the frequency range 50-1000 Hz and with the frequency's time derivative in the range -1.0E-8 Hz/s to zero. Data from the fourth LIGO science run (S4) have been used in this search. Three different semi-coherent methods of transforming and summing strain power from Short Fourier Transforms (SFTs) of the calibrated data have been used. The first, known as "StackSlide", averages normalized power from each SFT. A "weighted Hough" scheme is also developed and used, and which also allows for a multi-interferometer search. The third method, known as "PowerFlux", is a variant of the StackSlide method in which the power is weighted before summing. In both the weighted Hough and PowerFlux methods, the weights are chosen according to the noise and detector antenna-pattern to maximize the signal-to-noise ratio. The respective advantages and disadvantages of these methods are discussed. Observing no evidence of periodic gravitational radiation, we report upper limits; we interpret these as limits on this radiation from isolated rotating neutron stars. The best population-based upper limit with 95% confidence on the gravitational-wave strain amplitude, found for simulated sources distributed isotropically across the sky and with isotropically distributed spin-axes, is 4.28E-24 (near 140 Hz). Strict upper limits are also obtained for small patches on the sky for best-case and worst-case inclinations of the spin axes.Comment: 39 pages, 41 figures An error was found in the computation of the C parameter defined in equation 44 which led to its overestimate by 2^(1/4). The correct values for the multi-interferometer, H1 and L1 analyses are 9.2, 9.7, and 9.3, respectively. Figure 32 has been updated accordingly. None of the upper limits presented in the paper were affecte

A rigorous comparison of different planet detection algorithms

The idea of finding extrasolar planets (ESPs) through observations of drops in stellar brightness due to transiting objects has been around for decades. It has only been in the last ten years, however, that any serious attempts to find ESPs became practical. The discovery of a transiting planet around the star HD 209458 (Charbonneau et al. 2000) has led to a veritable explosion of research, because the photometric method is the only way to search a large number of stars for ESPs simultaneously with current technology. To this point, however, there has been limited research into the various techniques used to extract the subtle transit signals from noise, mainly brief summaries in various papers focused on publishing transit-like signatures in observations. The scheduled launches over the next few years of satellites whose primary or secondary science missions will be ESP discovery motivates a review and a comparative study of the various algorithms used to perform the transit identification, to determine rigorously and fairly which one is the most sensitive under which circumstances, to maximize the results of past, current, and future observational campaigns.Comment: Accepted for publications by Astronomy and Astrophysic

Automatic Detection of Punctual Errors in Multibeam Data Using a Robust Estimator

The Oceanographic and Hydrographic service of the Navy (SHOM) has been using two MultiBeam Echo-Sounders (MBES) since 1988. These systems enable swath coverage of the sea floor along a survey line.Compared with single beam Echo-sounder systems, the resolution of the data provided by these systems has been considerably increased. Nevertheless, errors still remain and they must be detected and eliminated to meet the international standards of bathymetric charts.The high volume of data, particularly in the case of very shallow water Echo-Sounder systems, makes manual validation of the data inappropriate. In order to reduce the operating costs of the data cleaning step, SHOM has developed algorithms to automatically detect huge datasets generated by MultiBeams.The algorithm described in this paper is based on a local modelization of the seabed. The fitting of a quadratic surface over the raw data is carried out using a robust estimator. We retained Tukey robust estimator as the most effective choice due to its adaptative capabilities. Possible outliers are soundings with high residual values between measured depths and depths estimated from the local model. Retained outliers are deduced from this first outliers set, by computing local cross validation.This algorithm has been tested on different bathymetric data sets. Its efficiency has been demonstrated whatever the depth or type of seabed. Moreover, its application only requires two parameters to be set, thus making it the obvious choice. It has currently been adopted and installed on board all the SHOM's ships

Error Detection in the Active Front Steering System

Parameters to the CUSUM algorithm are calculated with the aid of the statistical properties of the input data and change detection theory. It works on Gaussian sequences but not on the data from the measurements. Also an approach called Local CUSUM, which lets detectors act over different intervals of the input data is presented and turned out to give good results. These approaches are compared to other detectors such as GMA. The detectors have been used to detect errors in the actuator dynamics of the Active Front Steering system and to detect erroneous wheel velocities in a car

Damage identification in civil engineering infrastructure under operational and environmental conditions

Tese de doutoramento. Engenharia Civil. Faculdade de Engenharia. Universidade do Porto. 201

Confounding factors analysis and compensation for highspeed bearing diagnostics

In recent years, machine diagnostics through vibration monitoring is gaining a rising interest. Indeed, in the literature many advanced techniques are available to disclose the fault establishment as well as damage type, location and severity. Unfortunately, in general, these high-level algorithms are not robust to operational and environmental variables, restricting the field of applicability of machine diagnostics. Most of industrial machines in fact, work with variable loads, at variable speeds and in uncontrolled environments, so that the finally measured signals are often non-stationary. The very common time-series features based on statistical moments (such as root mean square, skewness, kurtosis, peak value and crest factor) undergo variations related to changes in the machine operational parameters (e.g. speed, load, …) or in the environmental parameters (e.g. temperature, humidity, …), which can be seen as non-measured, and then latent, confounding factors with respect to the health information of interest. In order to face such issue, statistical techniques like (in a first exploratory stage) the Principal Component Analysis, or the Factor Analysis, are available. The pursuit of features insensitive to these factors, can be also tackled exploiting the cointegration property of non-stationary signals. In this paper, the most common methods for reducing the influence of latent factors are considered, and applied to investigate the data collected over the rig available at the DIRG laboratory, specifically conceived to test high speed aeronautical bearings monitoring vibrations by means of 2 tri-axial accelerometers while controlling the rotational speed (0 – 30000 RPM), the radial load (0 to 1800 N) and recording the lubricant oil temperature. The compensation scheme is based on two procedures which are established in univariate analyses, but not so well documented in multivariate cases: the removal of deterministic trends by subtraction of a regression, and the removal of stochastic trends in difference stationary series by subtraction of the one-step ahead prediction from an autoregressive model. The extension of these methods to the multivariate case is here analysed to find an effective way of enhancing damage patterns when the masking effect due to the non-stationarities induced by latent factors is strong

A Normalized Fractionally Lower-Order Moment Algorithm for Space-Time Adaptive Processing

A new space-time adaptive processing algorithm is proposed for clutter suppression in phased array radar systems. In contrast to the commonly used normalized least mean square (NLMS) algorithm which uses the second order moments of the data for adaptation, the proposed method uses the lower order moments of the data to adapt the weight coefficients. The normalization is also performed based on the data sample dispersion rather than the variance. Processing results using simulated and measured data show that the proposed algorithm converges faster than the NLMS algorithms in Gaussian and non-Gaussian clutter environments. It also provides better clutter suppression than the NLMS algorithm under heavy-tailed, impulsive, non-Gaussian environments. It in turn improves the target detection performance

All-sky search for periodic gravitational waves in LIGO S4 data

We report on an all-sky search with the LIGO detectors for periodic gravitational waves in the frequency range 50–1000 Hz and with the frequency’s time derivative in the range −1×10−8  Hz s−1 to zero. Data from the fourth LIGO science run (S4) have been used in this search. Three different semicoherent methods of transforming and summing strain power from short Fourier transforms (SFTs) of the calibrated data have been used. The first, known as StackSlide, averages normalized power from each SFT. A “weighted Hough” scheme is also developed and used, which also allows for a multi-interferometer search. The third method, known as PowerFlux, is a variant of the StackSlide method in which the power is weighted before summing. In both the weighted Hough and PowerFlux methods, the weights are chosen according to the noise and detector antenna-pattern to maximize the signal-to-noise ratio. The respective advantages and disadvantages of these methods are discussed. Observing no evidence of periodic gravitational radiation, we report upper limits; we interpret these as limits on this radiation from isolated rotating neutron stars. The best population-based upper limit with 95% confidence on the gravitational-wave strain amplitude, found for simulated sources distributed isotropically across the sky and with isotropically distributed spin axes, is 4.28×10−24 (near 140 Hz). Strict upper limits are also obtained for small patches on the sky for best-case and worst-case inclinations of the spin axes