Adaptive filtering techniques for gravitational wave interferometric data: Removing long-term sinusoidal disturbances and oscillatory transients

It is known by the experience gained from the gravitational wave detector proto-types that the interferometric output signal will be corrupted by a significant amount of non-Gaussian noise, large part of it being essentially composed of long-term sinusoids with slowly varying envelope (such as violin resonances in the suspensions, or main power harmonics) and short-term ringdown noise (which may emanate from servo control systems, electronics in a non-linear state, etc.). Since non-Gaussian noise components make the detection and estimation of the gravitational wave signature more difficult, a denoising algorithm based on adaptive filtering techniques (LMS methods) is proposed to separate and extract them from the stationary and Gaussian background noise. The strength of the method is that it does not require any precise model on the observed data: the signals are distinguished on the basis of their autocorrelation time. We believe that the robustness and simplicity of this method make it useful for data preparation and for the understanding of the first interferometric data. We present the detailed structure of the algorithm and its application to both simulated data and real data from the LIGO 40meter proto-type.Comment: 16 pages, 9 figures, submitted to Phys. Rev.

Information-driven Business Cycles: A Primal Approach

We develop a methodology to characterize equilibrium in DSGE models, free of parametric restrictions on information. First, we define a “primal” economy in which deviations from full information are captured by wedges in agents' expectations. Then, we provide conditions ensuring some information-structure can implement these wedges. We apply the approach to estimate a business cycle model where firms and households have dispersed information. The estimated model fits the data, attributing the majority of fluctuations to a single shock to households' expectations. The responses are consistent with an implementation in which households become optimistic about local productivities and gradually learn about others' optimism

Information-driven Business Cycles: A Primal Approach

We develop a methodology to characterize equilibrium in DSGE models, free of parametric restrictions on information. First, we define a “primal” economy in which deviations from full information are captured by wedges in agents' expectations. Then, we provide conditions ensuring some information-structure can implement these wedges. We apply the approach to estimate a business cycle model where firms and households have dispersed information. The estimated model fits the data, attributing the majority of fluctuations to a single shock to households' expectations. The responses are consistent with an implementation in which households become optimistic about local productivities and gradually learn about others' optimism