Explaining the GWSkyNet-Multi machine learning classifier predictions for gravitational-wave events

GWSkyNet-Multi is a machine learning model developed for classification of candidate gravitational-wave events detected by the LIGO and Virgo observatories. The model uses limited information released in the low-latency Open Public Alerts to produce prediction scores indicating whether an event is a merger of two black holes, a merger involving a neutron star, or a non-astrophysical glitch. This facilitates time sensitive decisions about whether to perform electromagnetic follow-up of candidate events during LIGO-Virgo-KAGRA (LVK) observing runs. However, it is not well understood how the model is leveraging the limited information available to make its predictions. As a deep learning neural network, the inner workings of the model can be difficult to interpret, impacting our trust in its validity and robustness. We tackle this issue by systematically perturbing the model and its inputs to explain what underlying features and correlations it has learned for distinguishing the sources. We show that the localization area of the 2D sky maps and the computed coherence versus incoherence Bayes factors are used as strong predictors for distinguishing between real events and glitches. The estimated distance to the source is further used to discriminate between binary black hole mergers and mergers involving neutron stars. We leverage these findings to show that events misclassified by GWSkyNet-Multi in LVK's third observing run have distinct sky area, coherence factor, and distance values that influence the predictions and explain these misclassifications. The results help identify the model's limitations and inform potential avenues for further optimization.Comment: 22 pages, 11 figures, submitted to Ap

Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about fundamental physics, cosmology and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO--Virgo network. We search for repeated signals from strong lensing by 1) performing targeted searches for subthreshold signals, 2) calculating the degree of overlap amongst the intrinsic parameters and sky location of pairs of signals, 3) comparing the similarities of the spectrograms amongst pairs of signals, and 4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by 1) frequency-independent phase shifts in strongly lensed images, and 2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the non-detection of gravitational-wave lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects

Search for eccentric black hole coalescences during the third observing run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M>70 M⊙) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0<e≤0.3 at 0.33 Gpc−3 yr−1 at 90\% confidence level

Reconstructing gravitational waves from core-collapse supernovae in Advanced LIGO-Virgo

Our current understanding of the core-collapse supernova (CCSN) explosion mechanism is incomplete, with multiple viable models for how the initial shock wave might be energized enough to lead to a successful explosion. Detection of a gravitational wave (GW) signal emitted in the initial few seconds after stellar core-collapse would provide unique and crucial insight into this process. With the Advanced LIGO and Advanced Virgo detectors expected to soon approach their design sensitivity, we could potentially detect this GW emission from a supernova within our galaxy. In anticipation of such a scenario, we study how well the BayesWave algorithm can recover the GW signal from CCSN models in simulated advanced detector noise, and optimize its ability to accurately reconstruct the signal waveforms. We find that BayesWave can confidently reconstruct the signal from a range of supernova explosion models in Advanced LIGO-Virgo for network signal-to-noise ratios > 30, reaching maximum reconstruction accuracies of ~ 90% at SNR ~ 100. For low SNR signals that are not confidently recovered, our optimization efforts result in gains in reconstruction accuracy of up to 20-40%, with typical gains of ~ 10%.Science, Faculty ofPhysics and Astronomy, Department ofGraduat

Prospects for reconstructing the gravitational-wave signals from core-collapse supernovae with Advanced LIGO-Virgo and the BayesWave algorithm

Our current understanding of the core-collapse supernova explosion mechanism is incomplete, with multiple viable models for how the initial shock wave might be energized enough to lead to a successful explosion. Detection of a gravitational-wave signal emitted in the initial few seconds after stellar core -collapse would provide unique and crucial insight into this process. With the Advanced LIGO and Advanced Virgo detectors expected to approach their design sensitivities soon, we could potentially detect this signal from a supernova within our galaxy. In anticipation of such a scenario, we study how well the BayesWave algorithm can recover the gravitational-wave signal from core-collapse supernova models in simulated advanced detector noise, and optimize its ability to accurately reconstruct the signal waveforms. We find that BayesWave can confidently reconstruct the signal from a range of supernova explosion models in Advanced LIGO-Virgo for network signal-to-noise ratios greater than or similar to 30, reaching maximum reconstruction accuracies of similar to 90% at SNR similar to 100. For low SNR signals that are not confidently recovered, our optimization efforts result in gains in reconstruction accuracy of up to 20%-40%, with typical gains of similar to 10%

GWSkyNet. II. A Refined Machine-learning Pipeline for Real-time Classification of Public Gravitational Wave Alerts

International audienceElectromagnetic follow-up observations of gravitational wave events offer critical insights and provide significant scientific gain from this new class of astrophysical transients. Accurate identification of gravitational wave candidates and rapid release of sky localization information are crucial for the success of these electromagnetic follow-up observations. However, searches for gravitational wave candidates in real time suffer from a nonnegligible false alarm rate. By leveraging the sky localization information and other metadata associated with gravitational wave candidates, GWSkyNet, a machine-learning classifier developed by Cabero et al., demonstrated promising accuracy for the identification of the origin of event candidates. We improve the performance of the classifier for LIGO–Virgo–KAGRA's (LVK) fourth observing run by reviewing and updating the architecture and features used as inputs by the algorithm. We also retrain and fine-tune the classifier with data from the third observing run. To improve the prospect of electromagnetic follow-up observations, we incorporate GWSkyNet into LVK's low-latency infrastructure as an automatic pipeline for the evaluation of gravitational wave alerts in real time. We test the readiness of the algorithm on an LVK mock data challenge campaign. The results show that by thresholding on the GWSkyNet score, noise masquerading as astrophysical sources can be rejected efficiently and the majority of true astrophysical signals can be correctly identified

Search for intermediate-mass black hole binaries in the third observing run of Advanced LIGO and Advanced Virgo

International audienceIntermediate-mass black holes (IMBHs) span the approximate mass range 100−105 M⊙, between black holes (BHs) that formed by stellar collapse and the supermassive BHs at the centers of galaxies. Mergers of IMBH binaries are the most energetic gravitational-wave sources accessible by the terrestrial detector network. Searches of the first two observing runs of Advanced LIGO and Advanced Virgo did not yield any significant IMBH binary signals. In the third observing run (O3), the increased network sensitivity enabled the detection of GW190521, a signal consistent with a binary merger of mass ∼150 M⊙ providing direct evidence of IMBH formation. Here, we report on a dedicated search of O3 data for further IMBH binary mergers, combining both modeled (matched filter) and model-independent search methods. We find some marginal candidates, but none are sufficiently significant to indicate detection of further IMBH mergers. We quantify the sensitivity of the individual search methods and of the combined search using a suite of IMBH binary signals obtained via numerical relativity, including the effects of spins misaligned with the binary orbital axis, and present the resulting upper limits on astrophysical merger rates. Our most stringent limit is for equal mass and aligned spin BH binary of total mass 200 M⊙ and effective aligned spin 0.8 at 0.056 Gpc−3 yr−1 (90% confidence), a factor of 3.5 more constraining than previous LIGO-Virgo limits. We also update the estimated rate of mergers similar to GW190521 to 0.08 Gpc−3 yr−1.Key words: gravitational waves / stars: black holes / black hole physicsCorresponding author: W. Del Pozzo, e-mail: [email protected]† Deceased, August 2020