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Abstract Noise in various interferometer systems can sometimes couple non-linearly to create excess noise in the gravitational wave (GW) strain data. Third-order statistics, such as bicoherence and biphase, can identify these couplings and help discriminate those occurrences from astrophysical GW signals. However, the conventional analysis can yield large bicoherence values even when no phase-coupling is present, thereby, resulting in false identifications. Introducing artificial phase randomization in computing the bicoherence reduces such occurrences with negligible impact on its effectiveness for detecting true phase-coupled disturbances. We demonstrate this property with simulated disturbances—focusing only on short-duration ones (lasting up to a few seconds) and employing mainly the auto-bicoherence in this work. Statistical hypothesis testing is used for distinguishing phase-coupled disturbances from non-phase coupled ones when employing the phase-randomized bicoherence. We also obtain an expression for the bicoherence value that minimizes the sum of the probabilities of false positives and false negatives. This can be chosen as a threshold for shortlisting bicoherence triggers for further scrutiny for the presence of non-linear coupling. Finally, the utility of the phase-randomized bicoherence analysis in GW time-series data is demonstrated for the following three scenarios: (1) Finding third-order statistical similarities within categories of noise transients, such as blips and koi fish. If these non-Gaussian noise transients, or glitches, have a common source, their bicoherence maps can have similarities arising from common bifrequencies related to that source. (2) Differentiating linear or non-linear phase-coupled glitches from compact binary coalescence signals through their bicoherence maps. This is explained with a simulated signal. (3) Identifying repeated bifrequencies in the second and third observation runs (i.e. O2 and O3) of LIGO and Virgo. 

ABSTRACT In this study, we investigate the impact of microlensing on gravitational wave (GW) signals in the LIGO−Virgo sensitivity band. Microlensing caused by an isolated point lens, with (redshifted) mass ranging from MLz ∈ (1,  105) M⊙ and impact parameter y ∈ (0.01,  5), can result in a maximum mismatch of $$\sim 30~{{\ \rm per\ cent}}$$ with their unlensed counterparts. When y < 1, it strongly anticorrelates with the luminosity distance enhancing the detection horizon and signal-to-noise ratio (SNR). Biases in inferred source parameters are assessed, with in-plane spin components being the most affected intrinsic parameters. The luminosity distance is often underestimated, while sky-localization and trigger times are mostly well-recovered. Study of a population of microlensed signals due to an isolated point lens primarily reveals: (i) using unlensed templates during the search causes fractional loss (20 per cent to 30 per cent) of potentially identifiable microlensed signals; (ii) the observed distribution of y challenges the notion of its high improbability at low values (y ≲ 1), especially for y ≲ 0.1; (iii) Bayes factor analysis of the population indicates that certain region in MLz − y parameter space have a higher probability of being detected and accurately identified as microlensed. Notably, the microlens parameters for the most compelling candidate identified in previous microlensing searches, GW200208_130117, fall within a 1σ range of the aforementioned higher probability region. Identifying microlensing signatures from MLz < 100 M⊙ remains challenging due to small microlensing effects at typical SNR values. Additionally, we also examined how microlensing from a population of microlenses influences the detection of strong lensing signatures in pairs of GW events, particularly in the posterior-overlap analysis. 

Gravitational waves (GWs) from the inspiral of binary compact objects offer a one-step measurement of the luminosity distance to the event, which is essential for the measurement of the Hubble constant, 𝐻0, which characterizes the expansion rate of the Universe. However, unlike binary neutron stars, the inspiral of binary black holes is not expected to be accompanied by electromagnetic radiation and a subsequent determination of its redshift. Consequently, independent redshift measurements of such GW events are necessary to measure 𝐻0. In this study, we present a novel Bayesian approach to infer 𝐻0 by measuring the overdensity of galaxies around individual binary black hole merger events in configuration space. We model the measured overdensity using the 3D cross-correlation between galaxies and GW events, explicitly accounting for the GW event localization uncertainty. We demonstrate the efficacy of our method with 250 simulated GW events distributed within 1 Gpc in colored Gaussian noise of Advanced LIGO and Advanced Virgo detectors operating at O4 sensitivity. We show that such measurements can constrain the Hubble constant with a precision of ≲8% (90% highest density interval). We highlight the potential improvements that need to be accounted for in further studies before the method can be applied to real data. 

Subjected to the tidal field of its companion, each component of a coalescing binary suffers a slow change in its mass (tidal heating) and spin (tidal torquing) during the inspiral and merger. For black holes, these changes are associated with their absorption of energy and angular momentum fluxes. This effect modifies the inspiral rate of the binary, and consequently, the phase and amplitude of its gravitational waveform. Numerical relativity (NR) waveforms contain these effects inherently, whereas analytical approximants for the early inspiral phase have to include them manually in the energy balance equation. In this work, we construct IMRPhenomD_Horizon, a frequency-domain gravitational waveform model that incorporates the effects of tidal heating of black holes. This is achieved by recalibrating the inspiral phase of the waveform model IMRPhenomD to incorporate the phase corrections for tidal heating. We also include corrections to the amplitude, but add them directly to the inspiral amplitude model of IMRPhenomD. First we demonstrate that the inclusion of the corrections, especially in the phase, confers an overall improvement in the phase agreement between the analytical inspiral model (uncalibrated SEOBNRv2) and NR data. The model presented here is faithful, with less than 1% mismatches against a set of hybrid waveforms (except for one outlier that barely breaches this limit). The recalibrated model shows mismatches of up to ∼14% with IMRPhenomD for high mass ratios and spins. Amplitude corrections become less significant for higher mass ratios, whereas the phase corrections leave more impact—suggesting that the former is practically irrelevant for gravitational wave data analysis in Advanced LIGO (aLIGO), Virgo and KAGRA. Comparing with a set of 219 numerical relativity waveforms, we find that the median of mismatches decreases by ∼4% in aLIGO zero-detuned high power noise curve, and by ∼1.5% with a flat noise curve. This implies a modest but notable improvement in waveform accuracy. 

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.