Search

Despite the observation of nearly 100 compact binary coalescence (CBC) events up to the end of the Advanced gravitational-wave (GW) detectors' third observing run (O3), there remain fundamental open questions regarding their astrophysical formation mechanisms and environments. Population analysis should yield insights into these questions, but requires careful control of uncertainties and biases. GW observations have a strong selection bias: this is due first to the dependence of the signal amplitude on the source's (intrinsic and extrinsic) parameters, and second to the complicated nature of detector noise and of current detection methods. In this work, we introduce a new physically-motivated model of the sensitivity of GW searches for CBC events, aimed at enhancing the accuracy and efficiency of population reconstructions. In contrast to current methods which rely on re-weighting simulated signals (injections) via importance sampling, we model the probability of detection of binary black hole (BBH) mergers as a smooth, analytic function of source masses, orbit-aligned spins, and distance, fitted to accurately match injection results. The estimate can thus be used for population models whose signal distribution over parameter space differs significantly from the injection distribution. Our method has already been used in population studies such as reconstructing the BBH merger rate dependence on redshift., Comment: 21 pages, 10 figures

We present several high-accuracy surrogate models for gravitational-wave signals from equal-mass head-on mergers of Proca stars, computed through the Newman-Penrose scalar $\psi_4$. We also discuss the current state of the model extensions to mergers of Proca stars with different masses, and the particular challenges that these present. The models are divided in two main categories: two-stage and monolithic. In the two-stage models, a dimensional reduction algorithm is applied to embed the data in a reduced feature space, which is then interpolated in terms of the physical parameters. For the monolithic models, a single neural network is trained to predict the waveform from the input physical parameter. Our model displays mismatches below $10^{-3}$ with respect to the original numerical waveforms. Finally, we demonstrate the usage of our model in full Bayesian parameter inference through the accurate recovery of numerical relativity signals injected in zero-noise, together with the analysis of GW190521. For the latter, we observe excellent agreement with existing results that make use of full numerical relativity., Comment: 16 pages, 14 figures. v2: Matches published version

We assess the impact of accurate, self-consistent modelling of thermal effects in neutron-star merger remnants in the context of third-generation gravitational-wave detectors. This is done through the usage, in Bayesian model selection experiments, of numerical-relativity simulations of binary neutron star (BNS) mergers modelled through: a) nuclear, finite-temperature (or ``tabulated'') equations of state (EoSs), and b) their simplifed piecewise (or ``hybrid'') representation. These cover four different EoSs, namely SLy4, DD2, HShen and LS220. Our analyses make direct use of the Newman-Penrose scalar $\psi_4$ outputted by numerical simulations. Considering a detector network formed by three Cosmic Explorers, we show that differences in the gravitational-wave emission predicted by the two models are detectable with a natural logarithmic Bayes Factor $\log{\cal{B}}\geq 5$ at average distances of $d_L \simeq 50$Mpc, reaching $d_L \simeq 100$Mpc for source inclinations $\iota \leq 0.8$, regardless of the EoS. This impact is most pronounced for the HShen EoS. For low inclinations, only the DD2 EoS prevents the detectability of such modelling differences at $d_L \simeq 150$Mpc. Our results suggest that the usage a self-consistent treatment of thermal effects is crucial for third-generation gravitational wave detectors., Comment: 9 pages, 3 Figures

We investigate the joint primary mass, mass ratio, and redshift observed distribution of astrophysical black holes using the gravitational wave events detected by the LIGO-Virgo-KAGRA collaboration and included in the third gravitational wave transient catalogue. We reconstruct this distribution using Bayesian non-parametric methods, which are data-driven models able to infer arbitrary probability densities under minimal mathematical assumptions. We find evidence for the evolution with redshift of both the primary mass and mass ratio distribution: our analysis shows the presence of two distinct sub-populations in the primary mass - redshift plane, with the lighter population, $\lesssim$ 20 $M_\odot$, disappearing at higher redshifts, $z > 0.4$. The mass ratio distribution shows no support for symmetric binaries. The observed population of coalescing binary black holes evolves with look-back time, suggesting a trend in metallicity with redshift and/or the presence of multiple, redshift-dependent formation channels., Comment: 15 pages, 13 figures