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Abstract It has become increasingly useful to answer questions in gravitational-wave astronomy usingtransdimensionalmodels, where the number of free parameters can be varied depending on the complexity required to fit the data. Given the growing interest in transdimensional inference, we introduce a new package for the Bayesian inference Library (Bilby), calledtBilby. ThetBilbypackage allows users to set up transdimensional inference calculations using the existingBilbyarchitecture with off-the-shelf nested samplers and/or Markov Chain Monte Carlo algorithms. Transdimensional models are particularly helpful when seeking to test theoretically uncertain predictions described by phenomenological models. For example, bursts of gravitational waves can be modeled using a superposition ofNwavelets, whereNis itself a free parameter. Short pulses are modeled with small values ofN, whereas longer, more complicated signals are represented with a large number of wavelets stitched together. Other transdimensional models have been used to describe instrumental noise and the population properties of gravitational-wave sources. We provide a few demonstrations oftBilby, including fitting the gravitational-wave signal GW150914 with a superposition ofNsine-Gaussian wavelets. We outline our plans to further develop thetBilbycode suite for a broader range of transdimensional problems. 

ABSTRACT As the catalogue of gravitational-wave transients grows, several entries appear ‘exceptional’ within the population. Tipping the scales with a total mass of $$\sim 150 \,{\rm M}_\odot$$, GW190521 likely contained black holes in the pair-instability mass gap. The event GW190814, meanwhile, is unusual for its extreme mass ratio and the mass of its secondary component. A growing model-building industry has emerged to provide explanations for such exceptional events, and Bayesian model selection is frequently used to determine the most informative model. However, Bayesian methods can only take us so far. They provide no answer to the question: does our model provide an adequate explanation for exceptional events in the data? If none of the models we are testing provide an adequate explanation, then it is not enough to simply rank our existing models – we need new ones. In this paper, we introduce a method to answer this question with a frequentist p-value. We apply the method to different models that have been suggested to explain the unusually massive event GW190521: hierarchical mergers in active galactic nuclei and globular clusters. We show that some (but not all) of these models provide adequate explanations for exceptionally massive events like GW190521. 

Abstract Gravitational-wave observations of binary black holes have revealed unexpected structure in the black hole mass distribution. Previous studies employ physically motivated phenomenological models and infer the parameters that control the features of the mass distribution that are allowed in their model, associating the constraints on those parameters with their physical motivations a posteriori. In this work, we take an alternative approach in which we introduce a model parameterizing the underlying stellar and core-collapse physics and obtaining the remnant black hole distribution as a derived by-product. In doing so, we constrain the stellar physics necessary to explain the astrophysical distribution of black hole properties under a given model. We apply this to the mapping between initial mass and remnant black hole mass, accounting for mass-dependent mass loss using a simple parameterized description. Allowing the parameters of the initial mass–remnant mass relationship to evolve with redshift permits correlated and physically reasonable changes to features in the mass function. We find that the current data are consistent with no redshift evolution in the core–remnant mass relationship, but place only weak constraints on the change of these parameters. This procedure can be applied to modeling any physical process underlying the astrophysical distribution. We illustrate this by applying our model to the pulsational pair instability supernova (PPISN) process, previously proposed as an explanation for the observed excess of black holes at ∼35M_⊙. Placing constraints on the reaction rates necessary to explain the PPISN parameters, we concur with previous results in the literature that the peak observed at ∼35M_⊙is unlikely to be a signature from the PPISN process as presently understood. 

Abstract Recent work by Moroianu et al. has suggested that the binary neutron star (BNS) merger GW190425 might have a potential fast radio burst (FRB) counterpart association, FRB20190425A, at the 2.8σlevel of confidence with a likely host galaxy association, namely UGC10667. The authors argue that the observations are consistent with a long-lived hypermassive neutron star (HMNS) that formed promptly after the BNS merger and was stable for approximately 2.5 hr before promptly collapsing into a black hole. Recently, Bhardwaj et al. conclusively associated FRB20190425A with UGC10667, potentially providing a direct host galaxy candidate for GW190425. In this work, we examine the multimessenger association based on the spacetime localization overlaps between GW190425 and the FRB host galaxy UGC10667 and find that the odds for a coincident association are $\mathcal{O}\left(5\right)$. We validate this estimate by using a Gaussian process density estimator. Assuming that the association is indeed real, we then perform Bayesian parameter estimation on GW190425 assuming that the BNS event took place in UGC10667. We find that the viewing angle of GW190425 excludes an on-axis system atp(θ_v> 30°) ≈ 99.99%, highly favoring an off-axis system similar to GRB 170817A. We also find a slightly higher source frame total mass for the binary, namely, $m_{\mathrm{total}}={3.42}_{-0.11}^{+0.34}{M}_{\odot }$, leading to an increase in the probability of prompt collapse into a black hole and therefore disfavors the long-lived HMNS formation scenario. Given our findings, we conclude that the association between GW190425 and FRB20190425A is disfavoured by current state-of-the-art gravitational-wave analyses. 

ABSTRACT Observations of gravitational waves emitted by merging compact binaries have provided tantalizing hints about stellar astrophysics, cosmology, and fundamental physics. However, the physical parameters describing the systems (mass, spin, distance) used to extract these inferences about the Universe are subject to large uncertainties. The most widely used method of performing these analyses requires performing many Monte Carlo integrals to marginalize over the uncertainty in the properties of the individual binaries and the survey selection bias. These Monte Carlo integrals are subject to fundamental statistical uncertainties. Previous treatments of this statistical uncertainty have focused on ensuring that the precision of the inferred inference is unaffected; however, these works have neglected the question of whether sufficient accuracy can also be achieved. In this work, we provide a practical exploration of the impact of uncertainty in our analyses and provide a suggested framework for verifying that astrophysical inferences made with the gravitational-wave transient catalogue are accurate. Applying our framework to models used by the LIGO–Virgo–KAGRA collaboration and in the wider literature, we find that Monte Carlo uncertainty in estimating the survey selection bias is the limiting factor in our ability to probe narrow population models and this will rapidly grow more problematic as the size of the observed population increases. 

ABSTRACT Along their path from source to observer, gravitational waves may be gravitationally lensed by massive objects leading to distortion in the signals. Searches for these distortions amongst the observed signals from the current detector network have already been carried out, though there have as yet been no confident detections. However, predictions of the observation rate of lensing suggest detection in the future is a realistic possibility. Therefore, preparations need to be made to thoroughly investigate the candidate lensed signals. In this work, we present some follow-up analyses that could be applied to assess the significance of such events and ascertain what information may be extracted about the lens-source system by applying these analyses to a number of O3 candidate events, even if these signals did not yield a high significance for any of the lensing hypotheses. These analyses cover the strong lensing, millilensing, and microlensing regimes. Applying these additional analyses does not lead to any additional evidence for lensing in the candidates that have been examined. However, it does provide important insight into potential avenues to deal with high-significance candidates in future observations. 

Abstract Gravitational-wave observations of binary neutron star mergers provide valuable information about neutron star structure and the equation of state of dense nuclear matter. Numerous methods have been proposed to analyze the population of observed neutron stars, and previous work has demonstrated the necessity of jointly fitting the astrophysical distribution and the equation of state in order to accurately constrain the equation of state. In this work, we introduce a new framework to simultaneously infer the distribution of binary neutron star masses and the nuclear equation of state using Gaussian mixture model density estimates, which mitigates some of the limitations previously used methods suffer from. Using our method, we reproduce previous projections for the expected precision of our joint mass distribution and equation-of-state inference with tens of observations. We also show that mismodeling the equation of state can bias our inference of the neutron star mass distribution. While we focus on neutron star masses and matter effects, our method is widely applicable to population inference problems. 

Abstract Gravitational lensing by massive objects along the line of sight to the source causes distortions to gravitational wave (GW) 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 among the intrinsic parameters and sky location of pairs of signals, (3) comparing the similarities of the spectrograms among 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 nondetection of GW lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects. 

Since the first direct detection of gravitational waves by the LIGO–Virgo collaboration in 2015 (B. P. Abbott et al., 2016), the size of the gravitational-wave transient catalog has grown to nearly 100 events (R. Abbott et al., 2023), with the ongoing fourth observing run more than doubling the total number. Extracting astrophysical or cosmological information from these observations is a hierarchical Bayesian inference problem. GWPopulation is designed to provide simple-to-use, robust, and extensible tools for hierarchical inference in gravitational-wave astronomy or cosmology. It has been widely adopted for gravitational-wave astronomy, including producing flagship results for the LIGO-Virgo-KAGRA collaborations (Abac et al., 2024; R. Abbott et al., 2023). While designed to work with observations of compact binary coalescences, GWPopulation may be available to a wider range of hierarchical Bayesian inference problems.