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We uncover late-time gravitational-wave tails in fully nonlinear $3+1$dimensional numerical relativity simulations of merging black holes, using the highly accurate p code. We achieve this result by exploiting the strong magnification of late-time tails due to binary eccentricity, recently observed in perturbative evolutions, and showcase here the tail presence in head-on configurations for several mass ratios close to unity. We validate the result through a large battery of numerical tests and detailed comparison with a perturbative evolution, which display striking agreement with full nonlinear ones in the ringdown regime, and very similar tail morphologies. Our results offer yet another confirmation of the highly predictive power of black hole perturbation theory in the presence of a source, even when applied to nonlinear solutions. The late-time tail signal is much more prominent than anticipated until recently, and possibly within reach of gravitational-wave detector measurements, unlocking observational investigations of an additional set of general relativistic predictions on the long-range gravitational dynamics. 

We present a Bayesian framework for joint and coherent analyses of multimessenger binary neutron star signals. The method, implemented in our bajes infrastructure, incorporates a joint likelihood for multiple datasets, support for various semi-analytical kilonova models and numerical-relativity (NR) informed relations for the mass ejecta, as well as a technique to include and marginalize over modeling uncertainties. As a first application, we analyze the gravitational-wave GW170817 and the kilonova AT2017gfo data. These results are then combined with the most recent X-ray pulsars analyses of PSR J0030+0451 and PSR J0740+6620 to obtain EOS constraints.Various constraints on the mass-radius diagram and neutron star properties are then obtained by resampling over a set of ten million parametrized EOS built under minimal assumptions. We find that a joint and coherent approach improves the inference of the extrinsic parameters (distance) and, among the instrinc parameters, the mass ratio. The inclusion of NR informed relations strongly improves over the case of using an agnostic prior on the intrinsic parameters. Comparing Bayes factors, we find that the two observations are better explained by the common source hypothesis only by assuming NR-informed relations. These relations break some of the degeneracies in the employed kN models. The EOS inference folding-in PSR J0952-0607 minimum-maximum mass, PSR J0030+0451 and PSR J0740+6620 data constrains, among other quantities, the neutron star radius to R1.4=12.30−0.56+0.81R1.4​=12.30−0.56+0.81​ km (R1.4=13.20−0.90+0.91R1.4​=13.20−0.90+0.91​ km) and the maximum mass to Mmax=2.28−0.17+0.25 M⊙Mmax​=2.28−0.17+0.25​ M⊙​ (Mmax=2.32−0.19+0.30 M⊙Mmax​=2.32−0.19+0.30​ M⊙​) where the ST+PDT (PDT-U) analysis of Vinciguerra et a (2023) for PSR J0030+0451 is employed. Hence, the systematics on PSR J0030+0451 data reduction currently dominate the mass-radius diagram constraints.