Detection methods for non-Gaussian gravitational wave stochastic backgrounds

We address the issue of finding an optimal detection method for a discontinuous or intermittent gravitational wave stochastic background. Such a signal might sound something like popcorn popping. We derive an appropriate version of the maximum likelihood detection statistic, and compare its performance to that of the standard cross-correlation statistic both analytically and with Monte Carlo simulations. The maximum likelihood statistic performs better than the cross-correlation statistic when the background is sufficiently non-Gaussian. For both ground and space based detectors, this results in a gain factor, ranging roughly from 1 to 3, in the minimum gravitational-wave energy density necessary for detection, depending on the duty cycle of the background. Our analysis is exploratory, as we assume that the time structure of the events cannot be resolved, and we assume white, Gaussian noise in two collocated, aligned detectors. Before this detection method can be used in practice with real detector data, further work is required to generalize our analysis to accommodate separated, misaligned detectors with realistic, colored, non-Gaussian noise.Comment: 25 pages, 12 figures, submitted to physical review D, added revisions in response to reviewers comment

Edge-enhanced disruptive camouflage impairs shape discrimination

Disruptive colouration (DC) is a form of camouflage comprised of areas of pigmentation across a target’s surface that form false edges, which are said to impede detection by disguising the outline of the target. In nature, many species with DC also exhibit edge enhancement (EE); light areas have lighter edges and dark areas have darker edges. EE DC has been shown to undermine not only localisation but also identification of targets, even when they are not hidden (Sharman, Moncrieff, & Lovell, 2018). We use a novel task, where participants judge which “snake” is more “wiggly,” to measure shape discrimination performance for three colourations (uniform, DC, and EE DC) and two backgrounds (leafy and uniform). We show that EE DC impairs shape discrimination even when targets are not hidden in a textured background. We suggest that this mechanism may contribute to misidentification of EE DC targets

Adaptive detection of distributed targets in compound-Gaussian noise without secondary data: A Bayesian approach

In this paper, we deal with the problem of adaptive detection of distributed targets embedded in colored noise modeled in terms of a compound-Gaussian process and without assuming that a set of secondary data is available.The covariance matrices of the data under test share a common structure while having different power levels. A Bayesian approach is proposed here, where the structure and possibly the power levels are assumed to be random, with appropriate distributions. Within this framework we propose GLRT-based and ad-hoc detectors. Some simulation studies are presented to illustrate the performances of the proposed algorithms. The analysis indicates that the Bayesian framework could be a viable means to alleviate the need for secondary data, a critical issue in heterogeneous scenarios

Physics, Astrophysics and Cosmology with Gravitational Waves

Gravitational wave detectors are already operating at interesting sensitivity levels, and they have an upgrade path that should result in secure detections by 2014. We review the physics of gravitational waves, how they interact with detectors (bars and interferometers), and how these detectors operate. We study the most likely sources of gravitational waves and review the data analysis methods that are used to extract their signals from detector noise. Then we consider the consequences of gravitational wave detections and observations for physics, astrophysics, and cosmology.Comment: 137 pages, 16 figures, Published version <http://www.livingreviews.org/lrr-2009-2