Robust statistics for deterministic and stochastic gravitational waves in non-Gaussian noise I: Frequentist analyses

Gravitational wave detectors will need optimal signal-processing algorithms to extract weak signals from the detector noise. Most algorithms designed to date are based on the unrealistic assumption that the detector noise may be modeled as a stationary Gaussian process. However most experiments exhibit a non-Gaussian ``tail'' in the probability distribution. This ``excess'' of large signals can be a troublesome source of false alarms. This article derives an optimal (in the Neyman-Pearson sense, for weak signals) signal processing strategy when the detector noise is non-Gaussian and exhibits tail terms. This strategy is robust, meaning that it is close to optimal for Gaussian noise but far less sensitive than conventional methods to the excess large events that form the tail of the distribution. The method is analyzed for two different signal analysis problems: (i) a known waveform (e.g., a binary inspiral chirp) and (ii) a stochastic background, which requires a multi-detector signal processing algorithm. The methods should be easy to implement: they amount to truncation or clipping of sample values which lie in the outlier part of the probability distribution.Comment: RevTeX 4, 17 pages, 8 figures, typos corrected from first version

Subtraction-noise projection in gravitational-wave detector networks

In this paper, we present a successful implementation of a subtraction-noise projection method into a simple, simulated data analysis pipeline of a gravitational-wave search. We investigate the problem to reveal a weak stochastic background signal which is covered by a strong foreground of compact-binary coalescences. The foreground which is estimated by matched filters, has to be subtracted from the data. Even an optimal analysis of foreground signals will leave subtraction noise due to estimation errors of template parameters which may corrupt the measurement of the background signal. The subtraction noise can be removed by a noise projection. We apply our analysis pipeline to the proposed future-generation space-borne Big Bang Observer (BBO) mission which seeks for a stochastic background of primordial GWs in the frequency range $\sim 0.1-1$Hz covered by a foreground of black-hole and neutron-star binaries. Our analysis is based on a simulation code which provides a dynamical model of a time-delay interferometer (TDI) network. It generates the data as time series and incorporates the analysis pipeline together with the noise projection. Our results confirm previous ad hoc predictions which say that BBO will be sensitive to backgrounds with fractional energy densities below $\Omega=10^{-16}$Comment: 54 pages, 15 figure

Optimal generalization of power filters for gravitational wave bursts, from single to multiple detectors

Searches for gravitational wave signals which do not have a precise model describing the shape of their waveforms are often performed using power detectors based on a quadratic form of the data. A new, optimal method of generalizing these power detectors so that they operate coherently over a network of interferometers is presented. Such a mode of operation is useful in obtaining better detection efficiencies, and better estimates of the position of the source of the gravitational wave signal. Numerical simulations based on a realistic, computationally efficient hierarchical implementation of the method are used to characterize its efficiency, for detection and for position estimation. The method is shown to be more efficient at detecting signals than an incoherent approach based on coincidences between lists of events. It is also shown to be capable of locating the position of the source.Comment: 16 pages, 5 figure

Estimation for bilinear stochastic systems

Three techniques for the solution of bilinear estimation problems are presented. First, finite dimensional optimal nonlinear estimators are presented for certain bilinear systems evolving on solvable and nilpotent lie groups. Then the use of harmonic analysis for estimation problems evolving on spheres and other compact manifolds is investigated. Finally, an approximate estimation technique utilizing cumulants is discussed

A mathematical framework for critical transitions: normal forms, variance and applications

Critical transitions occur in a wide variety of applications including mathematical biology, climate change, human physiology and economics. Therefore it is highly desirable to find early-warning signs. We show that it is possible to classify critical transitions by using bifurcation theory and normal forms in the singular limit. Based on this elementary classification, we analyze stochastic fluctuations and calculate scaling laws of the variance of stochastic sample paths near critical transitions for fast subsystem bifurcations up to codimension two. The theory is applied to several models: the Stommel-Cessi box model for the thermohaline circulation from geoscience, an epidemic-spreading model on an adaptive network, an activator-inhibitor switch from systems biology, a predator-prey system from ecology and to the Euler buckling problem from classical mechanics. For the Stommel-Cessi model we compare different detrending techniques to calculate early-warning signs. In the epidemics model we show that link densities could be better variables for prediction than population densities. The activator-inhibitor switch demonstrates effects in three time-scale systems and points out that excitable cells and molecular units have information for subthreshold prediction. In the predator-prey model explosive population growth near a codimension two bifurcation is investigated and we show that early-warnings from normal forms can be misleading in this context. In the biomechanical model we demonstrate that early-warning signs for buckling depend crucially on the control strategy near the instability which illustrates the effect of multiplicative noise.Comment: minor corrections to previous versio

Signal-noise interaction in nonlinear optical fibers: a hydrodynamic approach

We present a new perturbative approach to the study of signal-noise interactions in nonlinear optical fibers. The approach is based on the hydrodynamic formulation of the nonlinear Schr\"odinger equation that governs the propagation of light in the fiber. Our method is discussed in general and is developed in more details for some special cases, namely the small-dispersion regime, the continuous-wave (CW) signal and the solitonic pulse. The accuracy of the approach is numerically tested in the CW case.Comment: Revised version, submitted to Optics express, 15 pages, 6 figure