Numerical-relativity simulations with non-trivial matter configurations require initial data that satisfy the Hamiltonian and momentum constraints of the Einstein equations. We construct constraint-satisfying scalar-field initial data using the eXtended Conformally Flat Condition (XCFC) formalism, in which the matter variables are conformally rescaled and an auxiliary vector field is introduced. In doing so, we overcome the issues of local uniqueness and convergence of the solutions that arise in the second-order elliptic equations associated with the constraints. Using an iterative solver method, we demonstrate the convergence of the XCFC approach to a solution for several scalar-field matter systems. Those include Gaussian-like profiles, topological torus configurations, and equal-mass boson star binaries. In particular, for the latter case, it is common to employ the superposition of two isolated boson star solutions in order to build the initial data. We show that our formalism significantly improves upon a superposition approach by generating genuinely constraint-satisfying initial data for boson star binaries.

Following a binary neutron star (BNS) merger, the transient remnant is often a fast spinning, differentially rotating, magnetized hypermassive neutron star (HMNS). This object is prone to the magnetorotational instability (MRI) which drives magnetohydrodynamic turbulence that significantly influences the HMNS global dynamics. A key consequence of turbulence is the outward transport of angular momentum which impacts the remnant's stability and lifetime. Most numerical simulations of BNS mergers are unable to resolve the MRI due to its inherently small wavelength. To overcome this limitation, subgrid models have been proposed to capture the effects of unresolved small-scale physics in terms of large-scale quantities. We present the first implementation of our MHD-Instability-Induced Turbulence (MInIT) model in global Newtonian simulations of MRI-sensitive, differentially rotating, magnetized neutron stars. Here, we show that by adding the corresponding turbulent stress tensors to the momentum equation, MInIT successfully reproduces the angular momentum transport in neutron stars driven by small-scale turbulence.

Mass estimates of white dwarfs via electromagnetic methods, often differ from those obtained through gravitational redshift measurements, in some cases with discrepancies ranging in 5%-15% across independent datasets. Although many of the discrepancies reported in large spectroscopic surveys and confirmed by high-precision techniques such as astrometric microlensing and wide-binary analyses may be attributable to thermal effects, model uncertainties or measurement errors prevent a complete description of some of the observations. Here, we explore an alternative explanation based on the presence of a gravitationally coupled bosonic scalar field that contributes to the stellar mass while remaining electromagnetically invisible. We construct stationary, static mixed configurations consisting of a white dwarf that presents a bosonic scalar field (dark matter) component, forming a composite white dwarf-boson star system. We explore families of solutions showing that a scalar field fraction of fDM 5%-15% to the mass contribution can account for the observed redshift excess. Our models provide a physically motivated explanation for the mass bias, might offer new observational signatures, and allow us to place preliminary constraints on the mass and compactness of the scalar field configuration. Finally, using our theoretical framework in combination with Bayesian model selection we provide plausible bounds for the mass of the constituent (ultralight) bosonic particle.

In a series of axisymmetric core-collapse supernova simulations extending up to <^>2 s, we identify a regime of precollapse central rotation rates (<^>'1 Hz) that greatly enhances the emission of gravitational waves (GWs) during extended periods of time after bounce. The enhancement is a consequence of the resonance between the frequency of the fundamental quadrupolar 2f mode of oscillation of the protoneutron star and the frequency of the epicyclic oscillations at the boundary of the inner core. We observe periods of about several hundred milliseconds each where the resonance is active. The GW emission enhancement produces a correlated resonant modulation of the associated neutrino signal at the same frequencies. With GW frequencies of O (1 kHz) and strain amplitudes within the sensitivity curves of current and next-generation interferometers at distances of O(1 Mpc), this resonant-amplification mechanism may represent a potential game changer for unveiling the supernova explosion mechanism through multimessenger astronomy.

Deep-learning methods are becoming increasingly important in gravitational-wave data analysis, yet their performance often relies on large training datasets and models whose internal representations are difficult to interpret. Sparse dictionary learning (SDL) offers a complementary approach: it performs well in scarce-data regimes and yields physically interpretable representations of gravitational-wave morphology. Here we present CLAWDIA (Comprehensive Library for the Analysis of Waves via Dictionary-based Algorithms), an open-source Python framework that integrates SDL-based denoising and classification under realistic detector noise. We systematise previously isolated SDL workflows into a unified, modular environment with a consistent, user-friendly interface. The current release provides several time-domain denoising strategies based on LASSO-regularised sparse coding and a classifier based on Low-Rank Shared Dictionary Learning. A companion toolbox, GWADAMA, supports dataset construction and realistic conditioning of real and simulated interferometer data. We demonstrate CLAWDIA's performance by denoising the signal from binary neutron star event GW170817 and by classifying families of instrumental glitches from LIGO's third observing run, highlighting robustness at low signal-to-noise ratios. CLAWDIA is intended as a community-driven, interoperable library extensible to additional tasks, including detection and parameter estimation.

The post-merger phase of binary neutron star (BNS) mergers encodes valuable information about the equation of state (EOS) of supranuclear matter. Extracting this information from the analysis of the post-merger waveforms remains challenging due to the high-frequency limitations of current detectors. Future third-generation observatories, such as the Einstein Telescope (ET) and NEMO, will have the sensitivity required to resolve post-merger signals with high fidelity. In this work, we apply CLAWDIA, our recently developed sparse dictionary learning (SDL) framework, to classify different EOS models using only the post-merger gravitational-wave emission of simulated BNS mergers available in the CoRe database. Our dataset comprises five EOS models representative of a broad range of neutron star properties. The SDL framework is optimised under realistic detection conditions by injecting signals into simulated noise matching the sensitivity curves of ET and NEMO. Our results show that classification is primarily driven by the dominant post-merger frequency, f_2, which encodes EOS-dependent information. At a modest signal-to-noise ratio of 5, our method achieves F_1 scores of 0.76 for ET and 0.70 for NEMO, with performance improving for higher signal-to-noise ratios. The reliability and generalisation capabilities of the model are assessed with additional tests, including the classification of an EOS not included in the training dataset and the analysis of detector-specific biases.

We present a framework for detecting gravitational-wave signals lensed by cosmic strings (CSs), addressing a key gap in current searches. CSs, whose detection would provide a unique probe of highenergy physics and the early Universe, possess distinct topological and geometric features that require a dedicated search strategy. Our approach employs a full-wave transmission factor, expressed analytically via Fresnel integrals, which captures the characteristic diffraction and interference effects of the conical spacetime around a straight CS. We contrast CS lensing with the well-studied point mass lens (PML) model, highlighting their fundamental differences: CS lensing depends on cosmological distances, string tension Delta, and wavelength lambda, and produces two nonamplified images set by the global conical geometry. In contrast, PML lensing is governed by the distance-independent ratio similar to MLZ/lambda, where MLZ represents the redshifted mass of the lens, with image properties derived from the lens equation. For binary black hole mergers lensed by CSs, we show that the waveforms exhibit a characteristic beating pattern or timeseparated exact replicas. We derive a detectability bound on the string tension and, using Bayesian model selection, demonstrate that CS lensing is distinguishable from both unlensed and PML-lensed signals across a wide region of parameter space.

We use physics-informed neural networks (PINNs) to solve the gravitational quasinormal-mode (QNM) eigenvalue problem for Kerr spacetime directly in the two-dimensional hyperboloidal formulation of the Teukolsky equation. This formulation does not require separation of variables and thus retains the coupled radial–angular structure. Such a scheme provides a prototype for calculating the QNMs of beyond-Kerr black holes for which the perturbation equations are non-separable. Sequences with increasing angular momentum are constructed, reaching close to the extremal limit. We focus on the fundamental modes (ℓ,m,n)=(2,0,0), (2,1,0), (2,2,0), (3,3,0) and (4,4,0), together with the first overtone (2,2,1). Independent benchmark evaluation shows that every reported real and imaginary frequency component remains below 0.5% error, with a median deviation of 0.1%. This accuracy is maintained in the near-extremal regime, where the damping rate becomes small and the modes are longest-lived. The results establish a non-spectral numerical route to multidimensional black-hole perturbation eigenproblems which does not match the substantially higher precision of dedicated Kerr solvers but offers greater flexibility and requires less analytical pre-processing. Non-separable rotating backgrounds and coupled systems, such as gravitational–electromagnetic Kerr–Newman perturbations, are natural extensions of the same construction.

In relativistic Astrophysics the I-Love-Q relations refer to approximately EoS-independent relations involving the moment of inertia, Love number, and quadrupole moment through some quantities that are normalised by the mass M_0 of the background configuration of the perturbative scheme. Since M_0 is not an observable quantity, this normalisation hinders the direct applicability of the relations. A common remedy assumes that M_0 coincides with the actual mass of the star M_S; however, this approximation is only adequate for very slow rotation (when the dimensionless spin parameter is χ_S<0.1). The more accurate alternative approach, based on the I-Love-Q-δM set of relations, circumvents this limitation by enabling the inference of M_0. Here we review both approaches and provide numerical comparisons.

We study the effect of shear viscosity in stationary magnetized accretion tori on synthetic images of non-rotating black hole shadows. Shear viscosity and spacetime-curvature contributions are introduced perturbatively in the tori through first and second-order transport coefficients within a second-order causal theory of non-ideal relativistic hydrodynamics. Synthetic black hole shadow images at 230 GHz are obtained via general relativistic radiative transfer computations assuming thermal synchrotron emission and for a wide range of plasma magnetization parameters, viewing inclination angles, electron-temperature prescriptions, and viscosity parameters. A comparative pixel-by-pixel analysis using two normalized metrics shows that the largest image differences occur for strongly magnetized tori. While shear viscosity induces only minor changes in the overall shadow morphology, its combined effects with spacetime curvature are more evident in localized modifications of the synchrotron emission and pixel-wise flux distribution. These effects become increasingly pronounced at higher inclination angles, with the largest brightness differences between viscous and non-viscous configurations occurring for larger values of the electron-temperature parameter. Overall, our results indicate that shear viscosity and spacetime curvature leave only modest imprints on black hole shadow images produced by stationary thick disks, with differences remaining at the level of a few μJy. Since our analysis is limited to stationary tori, the effects of shear viscosity might however be more significant in fully dynamical accretion systems.

AT2025ulz is an optical/near-infrared transient discovered during follow-up of the candidate gravitational wave (GW) event S250818k. Its young age (≲1 day), rapid decline, and strong color evolution over the first 48 hr classify it as a potential kilonova candidate. In this work, we present the results of our observing campaign, carried out with the Gran Telescopio Canarias and the Hubble Space Telescope (HST). Although the early time evolution of AT2025ulz resembles some aspects of a kilonova, its rapid onset (∼3 hr after the GW trigger) and luminosity (a factor of ∼5 brighter than AT2017gfo in g band) are difficult to reproduce. Only a small subset of our kilonova models matches its multicolor light curve, and the inferred ejecta mass is uncomfortably large given the low chirp mass (≲0.87 M _⊙ ) of the GW candidate. HST observations place the transient within a nearby ( z = 0.08489) spiral galaxy with ongoing star formation and measure a color (F336W − F160W ≈ 1.4 mag) that is too blue to match with a kilonova. Our data support the classification of AT2025ulz as a supernova, initially undergoing a shock-cooling phase and later entering its photospheric phase and spectroscopically identified via its broad absorption features.

Fermion-boson stars are compact equilibrium configurations composed of ordinary fermionic matter and a bosonic dark component interacting only through gravity. Such systems provide a natural framework for exploring deviations from standard neutron-star models, including the possible accumulation of dark matter inside neutron stars, and may be relevant for compact objects near the low-mass black-hole gap. We construct static and uniformly rotating fermion-boson stars within the framework of R-squared f(R) gravity, characterized by the functional form f(R)=R+aR^2, where a is a positive parameter governing the effective mass scale from the scalar degree of freedom. The fermionic sector is modeled as a perfect fluid described by a tabulated equation of state at zero temperature, while the bosonic component is represented by a self-interacting complex bosonic field. Our results show that the scalar degree of freedom modifies the spatial distribution of both the bosonic field and the fermionic pressure, enlarges the domain of admissible equilibrium solutions, and increases the maximum supported masses relative to general relativity. Our models remain compatible with current astrophysical and gravitational-wave constraints, suggesting that fermion-boson stars in R-squared gravity offer a promising framework to investigate the combined effects of dark bosonic matter, rotation, and strong-field modifications of gravity in compact objects.

The gravitational collapse of a massless scalar field remains a demanding benchmark for numerical methods in numerical relativity, as it exhibits critical behavior at the boundary between dispersion and black hole formation. In this work, we revisit this problem by relying on physics-informed neural networks (PINNs) as flexible solvers for partial differential equations, thereby providing a comparative assessment of several recent neural architectures. Building on the Einstein-massless-Klein-Gordon formulation in polar-areal coordinates, we consider four initial-value problems encompassing subcritical, critical, and supercritical regimes and use high-resolution finite-difference simulations as reference solutions. Our study is primarily comparative: we evaluate several state-of-the-art deep learning architectures, including vanilla and high-precision PINNs, sinusoidal-feature and quadratic-residual variants, and Kolmogorov-Arnold networks, all trained under a common loss design that encodes the field equations, boundary conditions, and causal time-space enforcement, together with a novel adaptive spacetime sampling. Within this framework, we also introduce ModPINN, a modest modification of standard PINNs that augments standard multilayer perceptrons with coordinate embeddings, quadratic layers, and other common ingredients in recent literature. This study shows that deep-learning-based methods can reproduce finite-difference solutions for the scalar field and the spacetime metric with competitive accuracy using significantly fewer collocation points than more traditional methodologies. While no single architecture dominates in all regimes, ModPINN achieves particularly stable and accurate solutions near criticality, indicating that suitably designed embeddings and adaptive sampling can enhance the robustness of PINNs for challenging gravitational-collapse scenarios.

Glitches frequently contaminate data in gravitational-wave detectors, complicating the observation and analysis of astrophysical signals. This work introduces VIGILant, an automatic pipeline for classification and visualization of glitches in the Virgo detector. Using a curated dataset of Virgo O3b glitches, two machine learning approaches are evaluated: tree-based models (Decision Tree, Random Forest and XGBoost) using structured Omicron parameters, and Convolutional Neural Networks (ResNet) trained on spectrogram images. While tree-based models offer higher interpretability and fast training, the ResNet34 model achieved superior performance, reaching a F1 score of 0.9772 and accuracy of 0.9833 in the testing set, with inference times of tens of milliseconds per glitch. The pipeline has been deployed for daily operation at the Virgo site since observing run O4c, providing the Virgo collaboration with an interactive dashboard to monitor glitch populations and detector behavior. This allows to identify low-confidence predictions, highlighting glitches requiring further attention.

We present a full 3D numerical evolution code to study neutron stars in massive-scalar-tensor theories. The code is embedded in the Einstein Toolkit framework and its implementation constitutes a modified version of the Baumgarte-Shapiro-Shibata-Nakamura formalism with an additional nonminimally coupled scalar field. The approach we follow preserves the standard hydrodynamic evolution for matter fields, allowing eventually for a straightforward inclusion of more microphysical effects and better flexibility. Using this code, we examine the gravitational collapse of rapidly rotating, scalarized neutron stars to a black hole by exploring the influence of the scalar field on the dynamical features of the process and on the gravitational-wave emission. We find that for the configurations studied in this work, there is an observational degeneracy in the tensorial gravitational-wave emission between collapsing scalarized stars and their counterparts in general relativity. However, this degeneracy can be broken through the emission of scalar radiation, which carries an energy of 10^-3 M_sun c^2. This is orders of magnitude higher than the quadrupolar emission ( 10^-7 M_sun c^2) and might be used as an observational probe of modified gravity. We also find that rapid rotation can enhance this signal, since fast rotating stars can sustain larger scalar field amplitudes.

The speed-up of parameter estimation is an active field of research in gravitational-wave data analysis. In this paper we present GP15, a deep-learning method that merges residual networks and normalizing flows into a general-purpose, image-based estimator of binary black hole (BBH) parameters. Building on our early work, we map BBH spectrograms from the Advanced LIGO and Advanced Virgo detectors to color channels in an RGB image amenable to be processed with residual networks. GP15 is trained on simulated data for BBH mergers obtained with the waveform approximant and tested for all three-detector events from the GWTC-3 and GWTC-2.1 catalogs reported by the LIGO-Virgo-KAGRA (LVK) collaboration. Overall, our model yields good agreement with the LVK results over most parameters. Our simple model can produce large amounts of posterior samples in the order of a second, complementing existing approaches with normalizing flows based on time or frequency representation of gravitational-wave data. We also discuss current shortcomings of our model and possible improvements for future extensions (e.g. including noise conditioning from the detectors' PSD or splitting the parameter space into intrinsic and extrinsic subspaces).

The generation of accurate waveforms from binary black hole (BBH) mergers is a major effort in Gravitational-Wave Astronomy. In recent years, machine-learning-based surrogate models for BBH waveforms have been proposed. Those offer the potential to dramatically accelerate waveform generation while maintaining accuracy competitive with that of traditional waveform approximants. In this work, we investigate the viability of autoencoders as generative models for gravitational-wave signals from quasi-circular BBH mergers. We introduce AESur3dq8, a novel surrogate waveform model based on autoencoders that enables the rapid and accurate construction of large template banks, producing millions of waveforms in under a second using modest computational resources. The model is trained on the numerical-relativity-informed surrogate NRHybSur3dq8 and subsequently fine-tuned using the SXS catalog of BBH simulations. We demonstrate that waveforms generated by AESur3dq8 achieve mismatches of order 10^-4 with respect to Numerical Relativity waveforms, and that parameter estimation performed with these templates yields results fully consistent with those reported by the LIGO-Virgo-KAGRA Collaboration for observed gravitational-wave events.

Numerical relativity (NR) simulations provide the most faithful representation of the gravitational waves (GWs) emitted by binary black hole (BBH) systems during merger. In the context of GW astronomy, tasks such as parameter estimation can require vast numbers of waveform evaluations per second across the entire parameter space. Since performing full NR simulations for each evaluation is not computationally feasible, interpolating methods for existing NR waveforms, known as surrogate models, have been developed with marked success. In this paper, we build on our previous work to introduce methods to train a fast surrogate model based on neural networks in order to generate BBH merger waveforms, including the fundamental (2,2) mode, as well as the (3,3), (2,1), (4,4), (3,2), (4,3) and (5,5) higher-order modes. Applying a pretraining step on approximant data before fine-tuning on NR data allows us to smooth out the parameter space, and making use of the parallelization ability of GPUs to project the NR waveforms in the inclination-phase (ι, ϕ) sphere during training allows the fitting of all the explored modes simultaneously. The developed surrogate model, , achieves average mismatches of the order of 10^-4, with the worst mismatch at 2.5×10^-3, and achieves throughput above 6×10^5 waveforms/second on an NVIDIA V100 GPU. Parameter estimation tests confirm the usefulness of the inclusion of higher modes.

We present a numerical relativity study of eccentric mergers of equal-mass rotating m = 1 Proca stars, focusing on their gravitational-wave (GW) emission. By systematically varying key binary parameters, such as the initial orbital boost, which determines the orbital angular momentum, and the relative phase between the stars, we examine how the internal phase structure of the Proca field influences the merger dynamics and the properties of the emitted GWs. Our simulations demonstrate that the relative phase has paramount impact on the postmerger evolution, resulting in prompt black hole formation accompanied by a transient Proca remnant, the formation of a hypermassive m = 1 Proca star or even the emergence of a dynamically unstable spinning m = 2 Proca star. Under certain conditions, the GW signal exhibits significant odd modes (e.g., the l = m = 3 mode) that are absent in conventional black hole mergers, potentially serving as unique signatures of these exotic objects. Our findings offer new insights into the phenomenology of bosonic star mergers and the potential astrophysical role of ultralight bosonic fields.

Bosonic stars,hypothetical astrophysical entities, are generally categorized into two primary classes based on the nature of their constituent particles: Einstein Klein Gordon stars, made up of massive scalar bosons, and Proca stars, their vector ”cousins”. Depending on the boson masses and field frequencies, these objects may exhibit properties of diffuse, massive structures, with sizes comparable to or even exceeding those of galaxies. This concept has inspired the bosonic dark matter halo hypothesis, providing a theoretical framework to effectively model the dark matter content of galactic halos. In this paper we build on our previous work to explore the possibility of using vector and scalar bosons to model the components of galactic dark matter halos and subhalos in order to reproduce the observed rotation curves of galaxies. By employing diverse combinations of those bosonic dark matter models in conjunction with observable data for a sample of galaxies, we show that our two component dark matter approach notably improves the agreement between observations and theoretical predictions with respect to our previous investigation. Our framework may shed new light on the enduring mystery surrounding the apparent matter deficit observed in dwarf and spiral galaxies.