Search for: All records

Abstract The collection of gravitational waves (GWs) that are either too weak or too numerous to be individually resolved is commonly referred to as the gravitational-wave background (GWB). A confident detection and model-driven characterization of such a signal will provide invaluable information about the evolution of the universe and the population of GW sources within it. We present a new, user-friendly, Python-based package for GW data analysis to search for an isotropic GWB in ground-based interferometer data. We employ cross-correlation spectra of GW detector pairs to construct an optimal estimator of the Gaussian and isotropic GWB, and Bayesian parameter estimation to constrain GWB models. The modularity and clarity of the code allow for both a shallow learning curve and flexibility in adjusting the analysis to one’s own needs. We describe the individual modules that make up pygwb , following the traditional steps of stochastic analyses carried out within the LIGO, Virgo, and KAGRA Collaboration. We then describe the built-in pipeline that combines the different modules and validate it with both mock data and real GW data from the O3 Advanced LIGO and Virgo observing run. We successfully recover all mock data injections and reproduce published results. 

One of the many contributions of Prof. Fettweis was the invention of wave digital filters. These filters are obtained from classical RLC filters, in particular doubly terminated lossless two-ports, by using some transformations. Namely, the voltages and currents in the circuit elements are transformed into wave-variables and then a bilinear transformation is performed. If the wave transformation is performed appropriately it results in a realizable digital filter structure which furthermore enjoys a number of robustness properties such as low passband sensitivity, low roundoff noise, and freedom from limit cycle oscillations. Prof. Fettweis and his colleagues also showed that these properties are due to the inheritence of passivity properties from the continuous-time domain into the digital filter domain. Subsequent to this landmark work, a number of researchers worked on the problem of obtaining robust digital filter structures without starting from continuoustime circuits. One of these is the structurally bounded or structurally passive class of digital filters. These structures are based on inducing structural passivity directly into the implementation and are therefore simpler, both conceptually and from a practical viewpoint. They are also more general and lead to new structures which have no natural connection to electrical circuits. This paper gives an overview of some of these developments.