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GW190521, the most massive binary black hole merger confidently detected by the LIGO-Virgo- KAGRA Collaboration, is the first gravitational-wave observation of an intermediate-mass black hole. The signal was followed approximately 34 days later by flare ZTF19abanrhr, detected in AGN J124942.3 þ 344929 by the Zwicky Transient Facility at the 78% spatial contour for GW190521’s sky localization. Using the GWTC-2.1 data release, we find that the association between GW190521 and flare ZTF19abanrhr as its electromagnetic counterpart is preferred over a random coincidence of the two transients with a log Bayes’ factor of 8.6, corresponding to an odds ratio of ∼5400∶1 for equal prior odds and ∼400∶1 assuming an astrophysical prior odds of 1=13. Given the association, the multimessenger signal allows for an estimation of the Hubble constant, finding H0 ¼ 102þ27 −25 km s−1 Mpc−1 when solely analyzing GW190521 and 79.2þ17.6 −9.6 km s−1 Mpc−1 assuming prior information from the binary neutron star merger GW170817, both consistent with the existing literature. 

The copious scientific literature produced after the detection of GW170817 electromagnetic counterpart demonstrated the importance of a prompt and accurate localization of the gravitational wave within the comoving volume. In this letter, we present figaro, a ready to use and publicly available software that relies on Bayesian non-parametrics. figaro is designed to run in parallel with parameter estimation algorithms to provide updated three-dimensional volume localization information. Differently from any existing algorithms, the analytical nature of the figaro reconstruction allows a ranking of the entries of galaxy catalogues by their probability of being the host of a gravitational wave event, hence providing an additional tool for a prompt electromagnetic follow up of gravitational waves. We illustrate the features of figaro on binary black holes as well as on GW170817. Finally, we demonstrate the robustness of figaro by producing so-called pp-plots and we present a method based on information entropy to assess when, during the parameter estimation run, it is reasonable to begin releasing skymaps.

 

We introduce (H)DPGMM, a hierarchical Bayesian non-parametric method based on the Dirichlet process Gaussian mixture model, designed to infer data-driven population properties of astrophysical objects without being committal to any specific physical model. We investigate the efficacy of our model on simulated data sets and demonstrate its capability to reconstruct correctly a variety of population models without the need of fine-tuning of the algorithm. We apply our method to the problem of inferring the black hole mass function given a set of gravitational wave observations from LIGO and Virgo, and find that the (H)DPGMM infers a binary black hole mass function that is consistent with previous estimates without the requirement of a theoretically motivated parametric model. Although the number of systems observed is still too small for a robust inference, (H)DPGMM confirms the presence of at least two distinct modes in the observed merging black hole mass function, hence suggesting in a model-independent fashion the presence of at least two classes of binary black hole systems.

 

ABSTRACT The joint detection of the gravitational wave GW170817, of the short γ-ray burst GRB170817A and of the kilonova AT2017gfo, generated by the the binary neutron star (NS) merger observed on 2017 August 17, is a milestone in multimessenger astronomy and provides new constraints on the NS equation of state. We perform Bayesian inference and model selection on AT2017gfo using semi-analytical, multicomponents models that also account for non-spherical ejecta. Observational data favour anisotropic geometries to spherically symmetric profiles, with a log-Bayes’ factor of ∼104, and favour multicomponent models against single-component ones. The best-fitting model is an anisotropic three-component composed of dynamical ejecta plus neutrino and viscous winds. Using the dynamical ejecta parameters inferred from the best-fitting model and numerical–relativity relations connecting the ejecta properties to the binary properties, we constrain the binary mass ratio to q < 1.54 and the reduced tidal parameter to $120\lt \tilde{\Lambda }\lt 1110$. Finally, we combine the predictions from AT2017gfo with those from GW170817, constraining the radius of a NS of 1.4 M⊙ to 12.2 ± 0.5 km (1σ level). This prediction could be further strengthened by improving kilonova models with numerical-relativity information.