Binary Black Hole Signals Research Articles

SLICK: Strong Lensing Identification of Candidates Kindred in gravitational wave data

ABSTRACT By the end of the next decade, we hope to have detected strongly lensed gravitational waves by galaxies or clusters. Although there exist optimal methods for identifying lensed signal, it is shown that machine learning (ML) algorithms can give comparable performance but are orders of magnitude faster than non-ML methods. We present the SLICK pipeline which comprises a parallel network based on deep learning. We analyse the Q-transform maps (QT maps) and the Sine-Gaussian projection (SGP) maps generated for the binary black hole signals injected in Gaussian as well as real noise. We compare our network performance with the previous work and find that the efficiency of our model is higher by a factor of 5 at a false positive rate of 0.001. Further, we show that including SGP maps with QT maps data result in a better performance than analysing QT maps alone. When combined with sky localization constraints, we hope to get unprecedented accuracy in the predictions than previously possible. We also evaluate our model on the real events detected by the LIGO–Virgo collaboration and find that, at a threshold of 0.75 our network correctly classifies all of them, consistent with non-detection of lensing.

Reconstruction of Binary Black Hole Harmonics in LIGO Using Deep Learning

Gravitational-wave signals from coalescing compact binaries in the LIGO and Virgo interferometers are primarily detected by the template-based matched filtering method. While this method is optimal for stationary and Gaussian data scenarios, its sensitivity is often affected by nonstationary noise transients in the detectors. Moreover, most of the current searches do not account for the effects of precession of black hole spins and higher-order waveform harmonics, focusing solely on the leading-order quadrupolar modes. This limitation impacts our search for interesting astrophysical sources, such as intermediate-mass black hole binaries and hierarchical mergers. Here we show, for the first time, that deep learning can be used for accurate waveform reconstruction of precessing binary black hole signals with higher-order modes. This approach can also be adapted into a rapid trigger generation algorithm to enhance online searches. Our model, tested on simulated injections in real LIGO noise from the third observing run (2019–2020) achieved a high degree of overlap with injected signals. This accuracy was consistent across a wide range of black hole masses and spin configurations chosen for this study. When applied to real gravitational-wave events, our model's reconstructions achieved between 85% and 98% overlap with those obtained by Coherent WaveBurst (unmodeled) and LALInference (modeled) analyses. These results suggest that deep learning is a potent tool for analyzing signals from a diverse catalog of compact binaries.

Denoising gravitational-wave signals from binary black holes with a dilated convolutional autoencoder

Abstract The broadband frequency output of gravitational-wave (GW) detectors is a non-stationary and non-Gaussian time series data stream dominated by noise populated by local disturbances and transient artifacts, which evolve on the same timescale as the GW signals and may corrupt the astrophysical information. We study a denoising algorithm dedicated to expose the astrophysical signals by employing a convolutional neural network in the encoder-decoder configuration, i.e. apply the denoising procedure of coalescing binary black hole signals to the publicly available LIGO O1 time series strain data. The denoising convolutional autoencoder neural network is trained on a dataset of simulated astrophysical signals injected into the real detector’s noise and a dataset of detector noise artifacts (‘glitches’), and its fidelity is tested on real GW events from O1 and O2 LIGO-Virgo observing runs.

Deep learning network to distinguish binary black hole signals from short-duration noise transients

Blip glitches, a type of short-duration noise transient in the LIGO--Virgo data, are a nuisance for the binary black hole (BBH) searches. They affect the BBH search sensitivity significantly because their time-domain morphologies are very similar, and that creates difficulty in vetoing them. In this work, we construct a deep-learning neural network to efficiently distinguish BBH signals from blip glitches. We introduce sine-Gaussian projection (SGP) maps, which are projections of GW frequency-domain data snippets on a basis of sine-Gaussians defined by the quality factor and central frequency. We feed the SGP maps to our deep-learning neural network, which classifies the BBH signals and blips. Whereas the BBH signals are simulated, the blips used are taken from real data throughout our analysis. We show that our network significantly improves the identification of the BBH signals in comparison to the results obtained using traditional-$\chi^2$ and sine-Gaussian $\chi^2$. For example, our network improves the sensitivity by 75% at a false-positive rate of $10^{-2}$ for BBHs with total mass in the range $[80,140]~M_{\odot}$ and SNR in the range $[3,8]$. Also, it correctly identifies 95% of the real GW events in GWTC-3. The computation time for classification is a few minutes for thousands of SGP maps on a single core. With further optimisation in the next version of our algorithm, we expect a further reduction in the computational cost. Our proposed method can potentially improve the veto process in the LIGO--Virgo GW data analysis and conceivably support identifying GW signals in low-latency pipelines.

Metric assisted stochastic sampling search for gravitational waves from binary black hole mergers

We present a novel gravitational-wave detection algorithm that conducts a matched-filter search stochastically across the compact binary parameter space rather than relying on a fixed bank of template waveforms. This technique is competitive with standard template-bank-driven pipelines in both computational cost and sensitivity. However, the complexity of the analysis is simpler, allowing for easy configuration and horizontal scaling across heterogeneous grids of computers. To demonstrate the method we analyze approximately one month of public LIGO data from July 27 00:00 2017 UTC–Aug 25 22:00 2017 UTC and recover eight known confident gravitational-wave candidates. We also inject simulated binary black hole signals to demonstrate the sensitivity.Received 1 November 2021Accepted 4 October 2022DOI:https://doi.org/10.1103/PhysRevD.106.084033© 2022 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasClassical black holesGravitational wave detectionGravitational wave sourcesGravitation, Cosmology & Astrophysics

Relative calibration of the LIGO and Virgo detectors using astrophysical events from their third observing run

We explore a method to assess the relative scale of the strain measured in the different detectors of the gravitational-wave (GW) network, using binary black hole (BBH) events detected during the third observing run (O3). The number of such signals is becoming sufficiently large to adopt a statistical approach based on the ratio of the signal-to-noise ratio of the events between the detectors and the number of observed events in each detector. We demonstrate the principle of the method on simulations of BBH signals and we present its application to published O3 events reported by the multi-band template analysis pipeline. Constraints on the relative calibration of the GW network for O3 are obtained at the level of between the two LIGO detectors and at the level of between the LIGO Livingston detector and the Virgo detector.

Search for binary black hole mergers in the third observing run of Advanced LIGO-Virgo using coherent WaveBurst enhanced with machine learning

In this work, we use the coherent WaveBurst (cWB) pipeline enhanced with machine learning (ML) to search for binary black hole (BBH) mergers in the Advanced LIGO-Virgo strain data from the third observing run (O3). We detect, with equivalent or higher significance, all gravitational-wave (GW) events previously reported by the standard cWB search for BBH mergers in the third GW Transient Catalog (GWTC-3). The ML-enhanced cWB search identifies five additional GW candidate events from the catalog that were previously missed by the standard cWB search. Moreover, we identify three marginal candidate events not listed in GWTC-3. For simulated events distributed uniformly in a fiducial volume, we improve the detection efficiency with respect to the standard cWB search by approximately $20\%$ for both stellar-mass and intermediate mass black hole binary mergers, detected with a false-alarm rate less than $1\,\mathrm{yr}^{-1}$. We show the robustness of the ML-enhanced search for detection of generic BBH signals by reporting increased sensitivity to the spin-precessing and eccentric BBH events as compared to the standard cWB search. Furthermore, we compare the improvement of the ML-enhanced cWB search for different detector networks.

Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy

Gravitational wave (GW) detection is now commonplace and as the sensitivity of the global network of GW detectors improves, we will observe $\mathcal{O}(100)$s of transient GW events per year. The current methods used to estimate their source parameters employ optimally sensitive but computationally costly Bayesian inference approaches where typical analyses have taken between 6 hours and 5 days. For binary neutron star and neutron star black hole systems prompt counterpart electromagnetic (EM) signatures are expected on timescales of 1 second -- 1 minute and the current fastest method for alerting EM follow-up observers, can provide estimates in $\mathcal{O}(1)$ minute, on a limited range of key source parameters. Here we show that a conditional variational autoencoder pre-trained on binary black hole signals can return Bayesian posterior probability estimates. The training procedure need only be performed once for a given prior parameter space and the resulting trained machine can then generate samples describing the posterior distribution $\sim 6$ orders of magnitude faster than existing techniques.

Bounding dark charges on binary black holes using gravitational waves

In models of minicharged dark matter associated with a hidden $U(1)$ symmetry, astrophysical black holes may acquire a "dark" charge, in such a way that the inspiral dynamics of binary black holes can be formally described by an Einstein-Maxwell theory. Charges enter the gravitational wave signal predominantly through a dipole term, but their effect is known to effectively first post-Newtonian order in the phase, which enables measuring the size of the charge-to-mass ratios, $|q_i/m_i|$, $i = 1,2$, of the individual black holes in a binary. We set up a Bayesian analysis to discover, or constrain, dark charges on binary black holes. After testing our framework in simulations, we apply it to selected binary black hole signals from the second Gravitational Wave Transient Catalog (GWTC-2), namely those with low masses so that most of the signal-to-noise ratio is in the inspiral regime. We find no evidence for charges on the black holes, and place typical 1-$\sigma$ bounds on the charge-to-mass ratios of $|q_i/m_i| \lesssim 0.2 - 0.3$.

Optimization of model independent gravitational wave search for binary black hole mergers using machine learning

The Coherent WaveBurst (cWB) search algorithm identifies generic gravitational wave (GW) signals in the LIGO-Virgo strain data. We propose a machine learning (ML) method to optimize the pipeline sensitivity to the special class of GW signals known as binary black hole (BBH) mergers. Here, we test the ML-enhanced cWB search on strain data from the first and second observing runs of Advanced LIGO and successfully recover all BBH events previously reported by cWB, with higher significance. For simulated events found with a false alarm rate less than $1\,\mathrm{yr}^{-1}$, we demonstrate the improvement in the detection efficiency of 26% for stellar-mass BBH mergers and 16% for intermediate mass black hole binary mergers. To demonstrate the robustness of the ML-enhanced search for the detection of generic BBH signals, we show that it has the increased sensitivity to the spin precessing or eccentric BBH events, even when trained on simulated quasi-circular BBH events with aligned spins.

Constraints on extended gravity models through gravitational wave emission

Using recent experimental results of detection of gravitational waves from the binary black hole signals by Advanced LIGO and Advanced Virgo, we investigate the propagation of gravitational waves in the context of fourth order gravity nonminimally coupled to a massive scalar field. Gravitational radiation admits extra massive modes of oscillation and we assume that the amplitude of these modes is comparable to that of the massless mode. We derive the propagation equation and effective mass for each degree of freedom and we infer, from the current observational data, constraints on the free parameters of the gravity models we considered. In particular, for f(R)=R-R2/R0, the constraint obtained from the speed of gravitational waves is not compatible with the one set by Solar System tests, which implies that amplitude of the massive modes could not be detectable with current experiments on Earth.

Reconstructing gravitational wave signals from binary black hole mergers with minimal assumptions

We present a systematic comparison of the binary black hole (BBH) signal waveform reconstructed by two independent and complementary approaches used in LIGO and Virgo source inference: a template-based analysis, and a morphology-independent analysis. We apply the two approaches to real events and to two sets of simulated observations made by adding simulated BBH signals to LIGO and Virgo detector noise. The first set is representative of the 10 BBH events in the first Gravitational Wave Transient Catalog (GWTC-1). The second set is constructed from a population of BBH systems with total mass and signal strength in the ranges that ground based detectors are typically sensitive. We find that the reconstruction quality of the GWTC-1 events is consistent with the results of both sets of simulated signals. We also demonstrate a simulated case where the presence of a mismodelled effect in the observed signal, namely higher order modes, can be identified through the morphology-independent analysis. This study is relevant for currently progressing and future observational runs by LIGO and Virgo.

Interpreting gravitational-wave burst detections: constraining source properties without astrophysical models

We show that for detections of gravitational-wave transients, constraints can be given on physical parameters of the source without using any specific astrophysical models. Relying only on fundamental principles of general relativity, we can set upper limits on the size, mass, and distance of the source solely from characteristics of the observed waveform. If the distance of the source is known from independent (e.g. electromagnetic) observations, we can also set lower limits on the mass and size. As a demonstration, we tested these constraints on binary black hole signals observed by the LIGO and Virgo detectors during their first and second observing runs, as well as on simulated binary black hole and core-collapse supernova signals reconstructed from simulated detector data. We have found that our constraints are valid for all analyzed source types, but their efficiency (namely, how far they are from the true parameter values) strongly depends on the source type, ranging from being in the same order of magnitude to a several orders of magnitude difference. In cases when a gravitational-wave signal is reconstructed without waveform templates and no astrophysical model on the source is available, these constraints provide the only quantitative characterization of the source that can guide the astrophysical modeling process.

Tests of general relativity with the binary black hole signals from the LIGO-Virgo catalog GWTC-1

The detection of gravitational waves by Advanced LIGO and Advanced Virgo provides an opportunity to test general relativity in a regime that is inaccessible to traditional astronomical observations and laboratory tests. We present four tests of the consistency of the data with binary black hole gravitational waveforms predicted by general relativity. One test subtracts the best-fit waveform from the data and checks the consistency of the residual with detector noise. The second test checks the consistency of the low- and high-frequency parts of the observed signals. The third test checks that phenomenological deviations introduced in the waveform model (including in the post-Newtonian coefficients) are consistent with zero. The fourth test constrains modifications to the propagation of gravitational waves due to a modified dispersion relation, including that from a massive graviton. We present results both for individual events and also results obtained by combining together particularly strong events from the first and second observing runs of Advanced LIGO and Advanced Virgo, as collected in the catalog GWTC-1. We do not find any inconsistency of the data with the predictions of general relativity and improve our previously presented combined constraints by factors of 1.1 to 2.5. In particular, we bound the mass of the graviton to be $m_g \leq 4.7 \times 10^{-23} \text{eV}/c^2$ ($90\%$ credible level), an improvement of a factor of 1.6 over our previously presented results. Additionally, we check that the four gravitational-wave events published for the first time in GWTC-1 do not lead to stronger constraints on alternative polarizations than those published previously.

Fundamental Physics Implications for Higher-Curvature Theories from Binary Black Hole Signals in the LIGO-Virgo Catalog GWTC-1.

Gravitational-wave astronomy offers not only new vistas into the realm of astrophysics, but it also opens an avenue for probing, for the first time, general relativity in its strong-field, nonlinear, and dynamical regime, where the theory's predictions manifest themselves in their full glory. We present a study of whether the gravitational-wave events detected so far by the LIGO-Virgo scientific collaborations can be used to probe higher-curvature corrections to general relativity. In particular, we focus on two examples: Einstein-dilaton-Gauss-Bonnet and dynamical Chern-Simons gravity. We find that the two events with a low-mass m≈7 M_{⊙} BH (GW151226 and GW170608) place stringent constraints on Einstein-dilaton-Gauss-Bonnet gravity, α_{EDGB}^{1/2}≲5.6 km, whereas dynamical Chern-Simons gravity remains unconstrained by the gravitational-wave observations analyzed.

Multi-detector null-stream-based statistic for compact binary coalescence searches

We develop a new multi-detector signal-based discriminator to improve the sensitivity of searches for gravitational waves from compact binary coalescences. The new statistic is the traditional computed on a null-stream synthesized from the gravitational-wave detector strain time-series of three detectors. This null-stream- statistic can be extended to networks involving more than three detectors as well. The null-stream itself was proposed as a discriminator between correlated unmodeled signals in multiple detectors, such as arising from a common astrophysical source, and uncorrelated noise transients. It can be useful even when the signal model is known, such as for compact binary coalescences. The traditional , on the other hand, is an effective discriminator when the signal model is known and lends itself to the matched-filtering technique. The latter weakens in its effectiveness when a signal lacks enough cycles in band; this can happen for high-mass black hole binaries. The former weakens when there are concurrent noise transients in different detectors in the network or the detector sensitivities are substantially different. Using simulated binary black hole signals, noise transients and strain for Advanced LIGO (in Livingston and Hanford) and Advanced Virgo detectors, we compare the performance of the null-stream- statistic with that of the traditional statistic using receiver-operating characteristics. The new statistic may form the basis for better signal-noise discriminators in multi-detector searches in the future.

Bayesian inference analysis of unmodelled gravitational-wave transients

We report the results of an in-depth analysis of the parameter estimation capabilities of , an algorithm for the reconstruction of gravitational-wave signals without reference to a specific signal model. Using binary black hole signals, we compare ’s performance to the theoretical best achievable performance in three key areas: sky localisation accuracy, signal/noise discrimination, and waveform reconstruction accuracy. is most effective for signals that have very compact time-frequency representations. For binaries, where the signal time-frequency volume decreases as the system mass increases, we find that ’s performance reaches or approaches theoretical optimal limits for system masses above approximately 50 . For such systems is able to localise the source on the sky as well as templated Bayesian analyses that rely on a precise signal model, and it is better than timing-only triangulation in all cases. We also show that the discrimination of signals against glitches and noise closely follows analytical predictions, and that only a small fraction of signals are discarded as glitches at a false alarm rate of 1/100 yr. Finally, the match between -reconstructed signals and injected signals is broadly consistent with first-principles estimates of the maximum possible accuracy, peaking at about for high mass systems and decreasing for lower-mass systems. These results demonstrate the potential of unmodelled signal reconstruction techniques for gravitational-wave astronomy.

Detection of (2,2) quasinormal mode from a population of black holes with a constructive summation method

The quasi normal modes (QNMs) associated with gravitational-wave signals from binary black hole (BBH) mergers can provide deep insight into the remnant's properties. Once design sensitivity is achieved, present ground-based gravitational wave interferometers could detect potentially hundreds of BBH signals in the coming years. For most, the ringdown phase will have a very weak signal-to-noise ratio (SNR). Signal summation techniques allow information extraction from the weak SNR ringdowns. We propose a method to constructively sum the (2,2) QNM from different BBH signals by synchronizing and rescaling them. The parameter space adopted to test the method is presently limited to mass ratio $q\leq3$, initially non-spinning black holes with face-on orientation. Moreover, since the synchronisation procedure fails for the weakest signals, we select all ringdowns with SNR above 2.6. Under these conditions, we show that for different BBH populations, 40 to 70% of all the potential detections could be used for the summation while still ensuring a summed SNR of $\sim$80% of the maximal achievable SNR (i.e. for ideally synchronized signals).

Matching Matched Filtering with Deep Networks for Gravitational-Wave Astronomy.

We report on the construction of a deep convolutional neural network that can reproduce the sensitivity of a matched-filtering search for binary black hole gravitational-wave signals. The standard method for the detection of well-modeled transient gravitational-wave signals is matched filtering. We use only whitened time series of measured gravitational-wave strain as an input, and we train and test on simulated binary black hole signals in synthetic Gaussian noise representative of Advanced LIGO sensitivity. We show that our network can classify signal from noise with a performance that emulates that of match filtering applied to the same data sets when considering the sensitivity defined by receiver-operator characteristics.

Parameter Estimation for Gravitational-wave Bursts with the BayesWave Pipeline.

We provide a comprehensive multi-aspect study of 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 (SGs), 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 to 30°.3. 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 SG wavelets, it is unsurprising that BW performs best in reconstructing SG and Gaussian waveforms. The BW accuracy in waveform reconstruction increases steeply with the network signal-to-noise ratio (S/Nnet), reaching a 85% and 95% match between the reconstructed and actual waveform below S/Nnet ≈ 20 and S/Nnet ≈ 50, respectively, for all morphologies. The BW accuracy in estimating central moments of waveforms is only limited by statistical errors in the frequency domain, and is also affected by systematic errors in the time domain as BW cannot reconstruct low-amplitude parts of signals that are overwhelmed by noise. The figures of merit we introduce can be used in future characterizations of parameter estimation pipelines.