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The first successful detection of gravitational waves by ground-based observatories, such as the Laser Interferometer Gravitational-Wave Observatory (LIGO), marked a breakthrough in our comprehension of the Universe. However, due to the unprecedented sensitivity required to make such observations, gravitational-wave detectors also capture disruptive noise sources called glitches, which can potentially be confused for or mask gravitational-wave signals. To address this problem, a community-science project, Gravity Spy, incorporates human insight and machine learning to classify glitches in LIGO data. The machine-learning classifier, integrated into the project since 2017, has evolved over time to accommodate increasing numbers of glitch classes. Despite its success, limitations have arisen in the ongoing LIGO fourth observing run (O4) due to the architecture's simplicity, which led to poor generalization and inability to handle multi-time window inputs effectively. We propose an advanced classifier for O4 glitches. Using data from previous observing runs, we evaluate different fusion strategies for multi-time window inputs, using label smoothing to counter noisy labels, and enhancing interpretability through attention module-generated weights. Our new O4 classifier shows improved performance, and will enhance glitch classification, aiding in the ongoing exploration of gravitational-wave phenomena. 

Abstract Gravitational waves (GWs) from merging compact objects encode direct information about the luminosity distance to the binary. When paired with a redshift measurement, this enables standard-siren cosmology: a Hubble diagram can be constructed to directly probe the Universe’s expansion. This can be done in the absence of electromagnetic measurements, as features in the mass distribution of GW sources provide self-calibrating redshift measurements without the need for a definite or probabilistic host galaxy association. This “spectral siren” technique has thus far only been applied with simple parametric representations of the mass distribution, and theoretical predictions for features in the mass distribution are commonly presumed to be fundamental to the measurement. However, the use of an inaccurate representation leads to biases in the cosmological inference, an acute problem given the current uncertainties in true source population. Furthermore, it is commonly presumed that the form of the mass distribution must be known a priori to obtain unbiased measurements of cosmological parameters in this fashion. Here, we demonstrate that spectral sirens can accurately infer cosmological parameters without such prior assumptions. We apply a flexible, nonparametric model for the mass distribution of compact binaries to a simulated catalog of 1000 GW signals, consistent with expectations for the next LIGO–Virgo–KAGRA observing run. We find that, despite our model’s flexibility, both the source mass model and cosmological parameters are correctly reconstructed. We predict a 11.2%✎measurement ofH₀, keeping all other cosmological parameters fixed, and a 6.4%✎measurement ofH(z= 0.9)✎when fitting for multiple cosmological parameters (1σuncertainties). This astrophysically agnostic spectral siren technique will be essential to arrive at precise and unbiased cosmological constraints from GW source populations. 

Abstract Gravitational-wave observations provide the unique opportunity of studying black hole formation channels and histories—but only if we can identify their origin. One such formation mechanism is the dynamical synthesis of black hole binaries in dense stellar systems. Given the expected isotropic distribution of component spins of binary black holes in gas-free dynamical environments, the presence of antialigned or in-plane spins with respect to the orbital angular momentum is considered a tell-tale sign of a merger’s dynamical origin. Even in the scenario where birth spins of black holes are low, hierarchical mergers attain large component spins due to the orbital angular momentum of the prior merger. However, measuring such spin configurations is difficult. Here, we quantify the efficacy of the spin parameters encoding aligned-spin (χ_eff) and in-plane spin (χ_p) at classifying such hierarchical systems. Using Monte Carlo cluster simulations to generate a realistic distribution of hierarchical merger parameters from globular clusters, we can infer mergers’χ_effandχ_p. The cluster populations are simulated using Advanced LIGO-Virgo sensitivity during the detector network’s third observing period and projections for design sensitivity. Using a “likelihood-ratio”-based statistic, we find that ∼2% of the recovered population by the current gravitational-wave detector network has a statistically significantχ_pmeasurement, whereas noχ_effmeasurement was capable of confidently determining a system to be antialigned with the orbital angular momentum at current detector sensitivities. These results indicate that measuring spin-precession throughχ_pis a more detectable signature of hierarchical mergers and dynamical formation than antialigned spins. 

On 2023 May 29, the LIGO-Virgo-KAGRA Collaboration observed a compact binary coalescence event consistent with a neutron star–black hole merger, though the heavier object of mass $$2.5-4.5\, {\rm M}_{\odot }$$ would fall into the purported lower mass gap. An alternative explanation for apparent observations of events in this mass range has been suggested as strongly gravitationally lensed binary neutron stars. In this scenario, magnification would lead to the source appearing closer and heavier than it really is. Here, we investigate the chances and possible consequences for the GW230529 event to be gravitationally lensed. We find this would require high magnifications and we obtain low rates for observing such an event, with a relative fraction of lensed versus unlensed observed events of $$2\times 10^{-3}$$ at most. When comparing the lensed and unlensed hypotheses accounting for the latest rates and population model, we find a $1/58$ chance of lensing, disfavouring this option. Moreover, when the magnification is assumed to be strong enough to bring the mass of the heavier binary component below the standard upper limits on neutron star masses, we find high probability for the lighter object to have a subsolar mass, making the binary even more exotic than a mass-gap neutron star–black hole system. Even when the secondary is not subsolar, its tidal deformability would likely be measurable, which is not the case for GW230529. Finally, we do not find evidence for extra lensing signatures such as the arrival of additional lensed images, type-II image dephasing, or microlensing. Therefore, we conclude it is unlikely for GW230529 to be a strongly gravitationally lensed binary neutron star signal. 

Abstract One of the goals of gravitational-wave astrophysics is to infer the number and properties of the formation channels of binary black holes (BBHs); to do so, one must be able to connect various models with the data. We explore benefits and potential issues with analyses using models informed by population synthesis. We consider five possible formation channels of BBHs, as in Zevin et al. (2021b). First, we confirm with the GWTC-3 catalog what Zevin et al. (2021b) found in the GWTC-2 catalog, i.e., that the data are not consistent with the totality of observed BBHs forming in any single channel. Next, using simulated detections, we show that the uncertainties in the estimation of the branching ratios can shrink by up to a factor of ∼1.7 as the catalog size increases from 50 to 250, within the expected number of BBH detections in LIGO–Virgo–KAGRA's fourth observing run. Finally, we show that this type of analysis is prone to significant biases. By simulating universes where all sources originate from a single channel, we show that the influence of the Bayesian prior can make it challenging to conclude that one channel produces all signals. Furthermore, by simulating universes where all five channels contribute but only a subset of channels are used in the analysis, we show that biases in the branching ratios can be as large as ∼50% with 250 detections. This suggests that caution should be used when interpreting the results of analyses based on strongly modeled astrophysical subpopulations. 

Abstract We present a population of 11 of the faintest (>25.5 AB mag) short gamma-ray burst (GRB) host galaxies. We model their sparse available observations using the stellar population inference codeProspector-βand develop a novel implementation to incorporate the galaxy mass–radius relation. Assuming these hosts are randomly drawn from the galaxy population and conditioning this draw on their observed flux and size in a few photometric bands, we determine that these hosts have dwarf galaxy stellar masses of $7.0\lesssim \log \left(M_{*}/M_{\odot }\right)\lesssim 9.1$. This is striking as only 14% of short GRB hosts with previous inferred stellar masses hadM_*≲ 10⁹M_⊙. We further show these short GRBs have smaller physical and host-normalized offsets than the rest of the population, suggesting that the majority of their neutron star (NS) merger progenitors were retained within their hosts. The presumably shallow potentials of these hosts translate to small escape velocities of ∼5.5–80 km s⁻¹, indicative of either low postsupernova systemic velocities or short inspiral times. While short GRBs with identified dwarf host galaxies now comprise ≈14% of the total Swift-detected population, a number are likely missing in the current population, as larger systemic velocities (observed from the Galactic NS population) would result in highly offset short GRBs and less secure host associations. However, the revelation of a population of short GRBs retained in low-mass host galaxies offers a natural explanation for the observedr-process enrichment via NS mergers in Local Group dwarf galaxies, and has implications for gravitational-wave follow-up strategies. 

Abstract Several features in the mass spectrum of merging binary black holes (BBHs) have been identified using data from the Third Gravitational Wave Transient Catalog (GWTC-3). These features are of particular interest as they may encode the uncertain mechanism of BBH formation. We assess if the features are statistically significant or the result of Poisson noise due to the finite number of observed events. We simulate catalogs of BBHs whose underlying distribution does not have the features of interest, apply the analysis previously performed on GWTC-3, and determine how often such features are spuriously found. We find that one of the features found in GWTC-3, the peak at ∼35M_☉, cannot be explained by Poisson noise alone: peaks as significant occur in 1.7% of catalogs generated from a featureless population. This peak is therefore likely to be of astrophysical origin. The data is suggestive of an additional significant peak at ∼10M_☉, though the exact location of this feature is not resolvable with current observations. Additional structure beyond a power law, such as the purported dip at ∼14M_☉, can be explained by Poisson noise. We also provide a publicly available package,GWMockCat, that creates simulated catalogs of BBH events with correlated measurement uncertainty and selection effects according to user-specified underlying distributions and detector sensitivities. 

Abstract The population of binary black hole mergers identified through gravitational waves has uncovered unexpected features in the intrinsic properties of black holes in the universe. One particularly surprising and exciting result is the possible existence of black holes in the pair-instability mass gap, ∼50–120 M ⊙ . Dense stellar environments can populate this region of mass space through hierarchical mergers, with the retention efficiency of black hole merger products strongly dependent on the escape velocity of the host environment. We use simple toy models to represent hierarchical merger scenarios in various dynamical environments. We find that hierarchical mergers in environments with high escape velocities (≳300 km s −1 ) are efficiently retained. If such environments dominate the binary black hole merger rate, this would lead to an abundance of high-mass mergers that is potentially incompatible with the empirical mass spectrum from the current catalog of binary black hole mergers. Models that efficiently generate hierarchical mergers, and contribute significantly to the observed population, must therefore be tuned to avoid a “cluster catastrophe” of overproducing binary black hole mergers within and above the pair-instability mass gap. 

Abstract There are few observed high-mass X-ray binaries (HMXBs) that harbor massive black holes (BHs), and none are likely to result in a binary black hole (BBH) that merges within a Hubble time; however, we know that massive merging BBHs exist from gravitational-wave (GW) observations. We investigate the role that X-ray and GW observational selection effects play in determining the properties of their respective detected binary populations. We find that, as a result of selection effects, detectable HMXBs and detectable BBHs form at different redshifts and metallicities, with detectable HMXBs forming at much lower redshifts and higher metallicities than detectable BBHs. We also find disparities in the mass distributions of these populations, with detectable merging BBH progenitors pulling to higher component masses relative to the full detectable HMXB population. Fewer than 3% of detectable HMXBs host BHs >35M_⊙in our simulated populations. Furthermore, we find the probability that a detectable HMXB will merge as a BBH system within a Hubble time is ≃0.6%. Thus, it is unsurprising that no currently observed HMXB is predicted to form a merging BBH with high probability. 

Abstract The Gravity Spy project aims to uncover the origins of glitches, transient bursts of noise that hamper analysis of gravitational-wave data. By using both the work of citizen-science volunteers and machine learning algorithms, the Gravity Spy project enables reliable classification of glitches. Citizen science and machine learning are intrinsically coupled within the Gravity Spy framework, with machine learning classifications providing a rapid first-pass classification of the dataset and enabling tiered volunteer training, and volunteer-based classifications verifying the machine classifications, bolstering the machine learning training set and identifying new morphological classes of glitches. These classifications are now routinely used in studies characterizing the performance of the LIGO gravitational-wave detectors. Providing the volunteers with a training framework that teaches them to classify a wide range of glitches, as well as additional tools to aid their investigations of interesting glitches, empowers them to make discoveries of new classes of glitches. This demonstrates that, when giving suitable support, volunteers can go beyond simple classification tasks to identify new features in data at a level comparable to domain experts. The Gravity Spy project is now providing volunteers with more complicated data that includes auxiliary monitors of the detector to identify the root cause of glitches.