Signal extraction out of background noise is a common challenge in high-precision physics experiments, where the measurement output is often a continuous data stream. To improve the signal-to-noise ...ratio of the detection, witness sensors are often used to independently measure background noises and subtract them from the main signal. If the noise coupling is linear and stationary, optimal techniques already exist and are routinely implemented in many experiments. However, when the noise coupling is nonstationary, linear techniques often fail or are suboptimal. Inspired by the properties of the background noise in gravitational wave detectors, this work develops a novel algorithm to efficiently characterize and remove nonstationary noise couplings, provided there exist witnesses of the noise source and of the modulation. In this work, the algorithm is described in its most general formulation, and its efficiency is demonstrated with examples from the data of the Advanced LIGO gravitational-wave observatory, where we could obtain an improvement of the detector gravitational-wave reach without introducing any bias on the source parameter estimation.
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We propose a hierarchical approach to testing general relativity with multiple gravitational wave detections. Unlike existing strategies, our method does not assume that parameters quantifying ...deviations from general relativity are either common or completely unrelated across all sources. We instead assume that these parameters follow some underlying distribution, which we parametrize and constrain. This can be then compared to the distribution expected from general relativity, i.e., no deviation in any of the events. We demonstrate that our method is robust to measurement uncertainties and can be applied to theories of gravity where the parameters beyond general relativity are related to each other, as generally expected. Our method contains the two extremes of common and unrelated parameters as limiting cases. We apply the hierarchical model to the population of 10 binary black hole systems so far detected by LIGO and Virgo. We do this for a parametrized test of gravitational wave generation, by modeling the population distribution of beyond-general-relativity parameters with a Gaussian distribution. We compute the mean and the variance of the population and show that both are consistent with general relativity for all parameters we consider. In the best case, we find that the population properties of the existing binary signals are consistent with general relativity at the ∼1% level. This hierarchical approach subsumes and extends existing methodologies and is more robust at revealing potential subtle deviations from general relativity with increasing number of detections.
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The Directional Isotropy of LIGO–Virgo Binaries Isi, Maximiliano; Farr, Will M.; Varma, Vijay
Astrophysical journal/The Astrophysical journal,
02/2024, Volume:
962, Issue:
1
Journal Article
Peer reviewed
Open access
Abstract We demonstrate how to constrain the degree of absolute alignment of the total angular momenta of LIGO–Virgo binary black holes, looking for a special direction in space that would break ...isotropy. We also allow for inhomogeneities in the distribution of black holes over the sky. Making use of dipolar models for the spatial distribution and orientation of the sources, we analyze 57 signals with false-alarm rates ≤1 yr −1 from the third LIGO–Virgo observing run. Accounting for selection biases, we find the population of LIGO–Virgo black holes to be consistent with both homogeneity and isotropy. We additionally find the data to constrain some directions of alignment more than others, discuss the interpretation of this measurement, and produce posteriors for the directions of total angular momentum of all binaries in our set. While our current constraints are weak, the fact that such a small number of detections can already yield a measurement suggests that this will be a powerful tool in the future; we explore this prospect with a number of simulated catalogs of varying size. All code and data are made publicly available at https://github.com/maxisi/gwisotropy/ .
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