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Register a new userThe BayesWave analysis pipeline in the era of gravitational wave observations
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We describe updates and improvements to the BayesWave gravitational wave transient analysis pipeline, and provide examples of how the algorithm is used to analyze data from ground-based gravitational wave detectors. BayesWave models gravitational wave signals in a morphology-independent manner through a sum of frame functions, such as Morlet-Gabor wavelets or chirplets. BayesWave models the instrument noise using a combination of a parametrized Gaussian noise component and non-stationary and non-Gaussian noise transients. Both the signal model and noise model employ trans-dimensional sampling, with the complexity of the model adapting to the requirements of the data. The flexibility of the algorithm makes it suitable for a variety of analyses, including reconstructing generic unmodeled signals; cross checks against modeled analyses for compact binaries; as well as separating coherent signals from incoherent instrumental noise transients (glitches). The BayesWave model has been extended to account for gravitational wave signals with generic polarization content and the simultaneous presence of signals and glitches in the data. We describe updates in the BayesWave prior distributions, sampling proposals, and burn-in stage that provide significantly improved sampling efficiency. We present standard review checks indicating the robustness and convergence of the BayesWave trans-dimensional sampler.
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The multi-band template analysis (MBTA) pipeline is a low-latency coincident analysis pipeline for the detection of gravitational waves (GWs) from compact binary coalescences. MBTA runs with a low computational cost, and can identify candidate GW events online with a sub-minute latency. The low computational running cost of MBTA also makes it useful for data quality studies. Events detected by MBTA online can be used to alert astronomical partners for electromagnetic follow-up. We outline the current status of MBTA and give details of recent pipeline upgrades and validation tests that were performed in preparation for the first advanced detector observing period. The MBTA pipeline is ready for the outset of the advanced detector era and the exciting prospects it will bring.
The global gravitational-wave detector network achieves higher detection rates, better parameter estimates, and more accurate sky localisation, as the number of detectors, $mathcal{I}$ increases. This paper quantifies network performance as a function of $mathcal{I}$ for BayesWave, a source-agnostic, wavelet-based, Bayesian algorithm which distinguishes between true astrophysical signals and instrumental glitches. Detection confidence is quantified using the signal-to-glitch Bayes factor, $mathcal{B}_{mathcal{S},mathcal{G}}$. An analytic scaling is derived for $mathcal{B}_{mathcal{S},mathcal{G}}$ versus $mathcal{I}$, the number of wavelets, and the network signal-to-noise ratio, SNR$_text{net}$, which is confirmed empirically via injections into detector noise of the Hanford-Livingston (HL), Hanford-Livingston-Virgo (HLV), and Hanford-Livingston-KAGRA-Virgo (HLKV) networks at projected sensitivities for the fourth observing run (O4). The empirical and analytic scalings are consistent; $mathcal{B}_{mathcal{S},mathcal{G}}$ increases with $mathcal{I}$. The accuracy of waveform reconstruction is quantified using the overlap between injected and recovered waveform, $mathcal{O}_text{net}$. The HLV and HLKV network recovers $87%$ and $86%$ of the injected waveforms with $mathcal{O}_text{net}>0.8$ respectively, compared to $81%$ with the HL network. The accuracy of BayesWave sky localisation is $approx 10$ times better for the HLV network than the HL network, as measured by the search area, $mathcal{A}$, and the sky areas contained within $50%$ and $90%$ confidence intervals. Marginal improvement in sky localisation is also observed with the addition of KAGRA.
A central challenge in Gravitational Wave Astronomy is identifying weak signals in the presence of non-stationary and non-Gaussian noise. The separation of gravitational wave signals from noise requires good models for both. When accurate signal models are available, such as for binary Neutron star systems, it is possible to make robust detection statements even when the noise is poorly understood. In contrast, searches for un-modeled transient signals are strongly impacted by the methods used to characterize the noise. Here we take a Bayesian approach and introduce a multi-component, variable dimension, parameterized noise model that explicitly accounts for non-stationarity and non-Gaussianity in data from interferometric gravitational wave detectors. Instrumental transients (glitches) and burst sources of gravitational waves are modeled using a Morlet-Gabor continuous wavelet frame. The number and placement of the wavelets is determined by a trans-dimensional Reversible Jump Markov Chain Monte Carlo algorithm. The Gaussian component of the noise and sharp line features in the noise spectrum are modeled using the BayesLine algorithm, which operates in concert with the wavelet model.
The detections of gravitational waves (GW) by LIGO/Virgo collaborations provide various possibilities to physics and astronomy. We are quite sure that GW observations will develop a lot both in precision and in number owing to the continuous works for the improvement of detectors, including the expectation to the newly joined detector, KAGRA, and the planned detector, LIGO-India. In this occasion, we review the fundamental outcomes and prospects of gravitational wave physics and astronomy. We survey the development focusing on representative sources of gravitational waves: binary black holes, binary neutron stars, and supernovae. We also summarize the role of gravitational wave observations as a probe of new physics.
We provide a comprehensive multi-aspect study on the performance of a pipeline used by the LIGO-Virgo Collaboration for estimating parameters of gravitational-wave bursts. We add simulated signals with four different morphologies (sine-Gaussians, Gaussians, white-noise bursts, and binary black hole signals) to simulated noise samples representing noise of the two Advanced LIGO detectors during their first observing run. We recover them with the BayesWave (BW) pipeline to study its accuracy in sky localization, waveform reconstruction, and estimation of model-independent waveform parameters. BW localizes sources with a level of accuracy comparable for all four morphologies, with the median separation of actual and estimated sky locations ranging from 25.1$^{circ}$ to 30.3$^{circ}$. This is a reasonable accuracy in the two-detector case, and is comparable to accuracies of other localization methods studied previously. As BW reconstructs generic transient signals with sine-Gaussian wavelets, it is unsurprising that BW performs the best in reconstructing sine-Gaussian and Gaussian waveforms. BWs accuracy in waveform reconstruction increases steeply with network signal-to-noise ratio (SNR$_{rm net}$), reaching a $85%$ and $95%$ match between the reconstructed and actual waveform below SNR$_{rm net} approx 20$ and SNR$_{rm net} approx 50$, respectively, for all morphologies. BWs accuracy in estimating central moments of waveforms is only limited by statistical errors in the frequency domain, and is affected by systematic errors too in the time domain as BW cannot reconstruct low-amplitude parts of signals overwhelmed by noise. The figures of merit we introduce can be used in future characterizations of parameter estimation pipelines.
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