arXiv subjects

José A. Font

Publications and source records attributed to José A. Font.

At least 37 records · Page 2Linked to original sources

arXiv2025

CLAWDIA: A dictionary learning framework for gravitational-wave data analysis

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.

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arXiv2025

Probing Supernovae through gravitational wave entropy

We study an entropy-based framework to analyze gravitational-wave signals from core-collapse supernovae. We use waveforms generated by numerical simulations and analyze them in both the time domain and the time-frequency domain using short-time Fourier and continuous wavelet transforms. From each representation, we compute four entropy measures -- Shannon, exponential, Rényi, and Tsallis -- and apply three feature selection methods to identify the most informative features. We then train machine-learning classifiers on these features to compare the performance of different methodological combinations. We find that the combination of Rényi entropy from the wavelet domain and the Relief-F selection method yields the most effective discrimination among different gravitational-wave signals.

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arXiv2025

Non-linear stability analysis of $\ell$-Proca stars

Vector boson stars, also known as Proca stars, exhibit remarkable dynamical robustness, making them strong candidates for potential astrophysical exotic compact objects. In search of theoretically well-motivated Proca star models, we recently introduced the $\ell$-Proca star, a multi-field extension of the spherical Proca star, whose $(2\ell + 1)$ constitutive fields have the same time and radial dependence, and their angular structure is given by all the available spherical harmonics for a fixed angular momentum number $\ell$. In this work, we conduct a non-linear stability analysis of these stars by numerically solving the Einstein-(multi, complex) Proca system for the case of $\ell = 2$, which are formed by five constitutive independent, complex Proca fields with $m = 0, |1|$, and $|2|$. Our analysis is based on long-term, fully non-linear, 3-dimensional numerical-relativity simulations without imposing any symmetry. We find that ($\ell=2$)-Proca stars are unstable throughout their entire domain of existence. In particular, we highlight that less compact configurations dynamically lose their global spherical symmetry, developing a non-axisymmetric $\tilde{m}=4$ mode instability and a subsequent migration into a new kind of multi-field Proca star formed by fields with different angular momentum number, $\ell=1$ and $\ell=2$, that we identify as unstable multi-$\ell$ Proca stars.

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arXiv2025

Reproducing galactic rotation curves with a two-component bosonic dark matter model

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.

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arXiv2025

Subgrid modelling of MRI-driven turbulence in differentially rotating neutron stars

Following a binary neutron star (BNS) merger, the transient remnant is often a fast-spinning, differentially rotating, magnetised 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, magnetised 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.

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arXiv2025

A Deep Learning Powered Numerical Relativity Surrogate for Binary Black Hole Waveforms

Gravitational-wave approximants are essential for gravitational-wave astronomy, allowing the coverage binary black hole parameter space for inference or match filtering without costly numerical relativity (NR) simulations, but generally trading some accuracy for computational efficiency. To reduce this trade-off, NR surrogate models can be constructed using interpolation within NR waveform space. We present a 2-stage training approach for neural network-based NR surrogate models. Initially trained on approximant-generated waveforms and then fine-tuned with NR data, these dual-stage artificial neural surrogate (\texttt{DANSur}) models offer rapid and competitively accurate waveform generation, generating millions in under 20ms on a GPU while keeping mean mismatches with NR around $10^{-4}$. Implemented in the \textsc{bilby} framework, we show they can be used for parameter estimation tasks.

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arXiv2025

Reducing the irreducible: the charged black hole bomb in a moving cavity

We revisit the charged black hole bomb by numerically solving the fully non-linear Einstein-Maxwell-(charged, complex) Klein-Gordon system with a moving mirror. By dynamically varying the cavity size, we find that the system evolves toward new hairy black hole equilibria. Expanding the mirror radius enhances superradiant extraction, increasing both the scalar field charge and the black hole's irreducible mass. Remarkably, on the other hand, shrinking the cavity size has the opposite effect: the black hole is able to reduce its irreducible mass as more charge than energy flows back from the field, without violating charge conservation or energy conditions. As a consistency check, in the limit of a vanishing cavity, we find that the system returns to the original Reissner-Nordström configuration. We discuss the implications of these findings for black hole thermodynamics in confined configurations where superradiant modes exist and the limitations of this setup, particularly in relation to Hawking's black hole area theorem.

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arXiv2025

Self-consistent treatment of thermal effects in neutron-star post-mergers: observational implications for third-generation gravitational-wave detectors

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 $ψ_4$ 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 $ι\leq 0.8$, 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.

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arXiv2025

Eccentric mergers of binary Proca stars

We present a numerical relativity study of eccentric mergers of equal-mass rotating $\bar m=1$ 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 post-merger evolution, resulting in prompt black hole formation accompanied by a transient Proca remnant, the formation of a hypermassive $\bar m=1$ Proca star or even the emergence of a dynamically-unstable spinning $\bar m=2$ Proca star. Under certain conditions, the GW signal exhibits significant odd-modes (e.g., the $\ell=m=3$ 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.

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arXiv2025

Parameter estimation of microlensed gravitational waves with Conditional Variational Autoencoders

Gravitational lensing of gravitational waves (GWs) provides a unique opportunity to study cosmology and astrophysics at multiple scales. Detecting microlensing signatures, in particular, requires efficient parameter estimation methods due to the high computational cost of traditional Bayesian inference. In this paper we explore the use of deep learning, namely Conditional Variational Autoencoders (CVAE), to estimate parameters of microlensed binary black hole (simulated) waveforms. We find that our CVAE model yields accurate parameter estimation and significant computational savings compared to Bayesian methods such as Bilby (up to five orders of magnitude faster inferences). Moreover, the incorporation of CVAE-generated priors into Bilby, based on the 95% confidence intervals of the CVAE posterior for the lensing parameters, reduces Bilby's average runtime by around 48% without any penalty on accuracy. Our results suggest that a CVAE model is a promising tool for future low-latency searches of lensed signals. Further applications to actual signals and integration with advanced pipelines could help extend the capabilities of GW observatories in detecting microlensing events.

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arXiv2025

Observing boson stars in binary systems: The case of Gaia BH1

The Gaia experiment recently reported the observation of a binary system composed of a Sun-like star orbiting a dark compact object, known as Gaia BH1. The nature of the compact object remains uncertain. While the Gaia mission identifies it as a black hole candidate, the absence of X-ray or radio detections challenges that interpretation, and alternative exotic compact objects such as boson stars have also been suggested. In this paper, we study whether a boson star could account for the observed properties of the source. To do so we compute the X-ray luminosity of the central dark object as a result of spherically symmetric (Bondi-Michel) accretion of matter, comparing our results for the cases in which the dark object is a Schwarzschild black hole or a non rotating boson star. Our model incorporates realistic interstellar medium properties, ranging from hot ionized gas to dense molecular clouds. By solving the governing equations numerically, we calculate mass accretion rates and derive the resulting Bremsstrahlung X-ray luminosities. Black holes and boson stars fundamentally differ by the absence of an event horizon in the latter, which directly impacts accretion dynamics as there is an accumulation of mass in regions closer to the boson star, which will significantly change the observed X-ray emission. For the Gaia BH1 system we find that accretion onto a black hole yields luminosities of $\sim10^{27} \ \text{erg}\, \text{cm}^{-2}\, \text{s}^{-1}$ which corresponds to an X-ray flux undetectable by Chandra sensitivity. On the other hand, boson star accretion can produce observable luminosities in the order of $10^{27} \ \text{to} \ 10^{41} \ \text{erg}\, \text{cm}^{-2}\, \text{s}^{-1}$.

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arXiv2025

Rapid detection of gravitational waves from binary black hole mergers using sparse dictionary learning

Current gravitational wave (GW) detection pipelines for compact binary coalescence based on matched-filtering have reported over 90 confident detections during the first three observing runs of the LIGO-Virgo-KAGRA (LVK) detector network. Decreasing the latency of detection, in particular for future detectors anticipated to have high detection rates, remains an ongoing effort. In this paper, we develop and test a sparse dictionary learning (SDL) algorithm for the rapid detection of GWs. We evaluate the algorithms biases and estimate its GW detection rate for an astrophysical population of binary black holes. The SDL algorithm is assessed using both, simulated data injected into the proposed A+ detector sensitivity and real data containing confident detections from the third LVK observing run. We find that our SDL algorithm can reconstruct a single binary black hole signal in less than 1 s. This suggests that SDL could be regarded as a promising approach for rapid, efficient GW detection in future observing runs of ground-based detectors.

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arXiv2025

Identifying thermal effects in neutron star merger remnants with model-agnostic waveform reconstructions and third-generation detectors

We probe the intrinsic differences in simulated gravitational-wave signals from binary neutron star (BNS) mergers, arising from varying approaches to incorporating thermal effects in numerical-relativity modeling. We consider a hybrid approach in which the equation of state (EOS) comprises a cold, zero-temperature, piecewise-polytropic part and a thermal part described by an ideal gas, and a tabulated approach based on self-consistent, microphysical, finite-temperature EOS. We use time-domain waveforms corresponding to BNS merger simulations with four different EOSs. Those are injected into Gaussian noise given by the sensitivity of the third-generation detector Einstein Telescope and reconstructed using BayesWave, a Bayesian data-analysis algorithm that recovers the signals through a model-agnostic approach. The two representations of thermal effects result in different dominant peak frequencies in the spectra of the postmerger signals, for both the quadrupole fundamental mode and the late-time inertial modes. For some of the EOSs investigated those differences are large enough to be told apart, especially in the early postmerger phase when the signal amplitude is the loudest. Our results suggest that a self-consistent treatment of thermal effects in BNS postmerger modeling is essential to prevent significant parameter biases in upcoming gravitational-wave detections.

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arXiv2024

Deep-Learning Classification and Parameter Inference of Rotational Core-Collapse Supernovae

We test deep-learning (DL) techniques for the analysis of rotational core-collapse supernovae (CCSN) gravitational-wave (GW) signals by performing classification and parameter inference of the maximum (peak) frequency and the GW strain amplitude ($Δh$) multiplied by the luminosity distance ($D$) attained at core bounce, respectively, $(f_{peak})$ and $(D \cdot Δh)$. Our datasets are built from a catalog of numerically generated CCSN waveforms assembled by Richers et al. 2017. Those waveforms are injected into noise from the Advanced Laser Interferometer Gravitational Wave Observatory and Advanced Virgo detectors corresponding to the O2 and O3a observing runs. For a network signal-to-noise ratio (SNR) above 5, our classification network using time series detects Galactic CCSN GW signals buried in detector noise with a false positive rate of 0.10% and a 98% accuracy, being able to detect all signals with SNR>10. The inference of $f_{peak}$ is more accurate than for $D \cdot Δh $, particularly for our datasets with the shortest time window (0.25 s) and for a minimum SNR=15. From the calibration plots of predicted versus true values of the two parameters, the standard deviation ($σ$) and the slope deviation with respect to the ideal value are computed. We find $σ_{D \cdot Δh} = 52.6$ cm and $σ_{f_{peak}} = 18.3$ Hz, with respective slope deviations of 11.6% and 8.3%. Our best model is also tested on waveforms from a recent CCSN catalog built by Mitra et al. 2023, different from the one used for the training. For these new waveforms, the true values of the two parameters are mostly within the $1σ$ band around the network's predicted values. Our results show that DL techniques hold promise to infer physical parameters of Galactic rotational CCSN events.

astro-ph.HE↗

arXiv2024

High-speed reconstruction of long-duration gravitational waves from extreme mass ratio inspirals using sparse dictionary learning

Measuring accurate long-duration gravitational waves from extreme mass ratio inspirals (EMRIs) could provide scientifically fruitful knowledge of massive black hole populations and robust tests for general relatively during the LISA mission. However, the immense computational requirements surrounding EMRI data processing and analysis makes their detection and analysis challenging. We further develop and explore a sparse dictionary learning (SDL) algorithm to expeditiously reconstruct EMRI gravitational waveforms lasting as long as 1 year. A suite of year-long EMRI systems are studied to understand the detection and accurate waveform retrieval prospects of the method. We show that full-year EMRIs can be reconstructed within 2 minutes, some with a false alarm rate less than 0.001/yr and with 1.16 day time windows with mismatch as low as 0.06. This provides an encouraging prospect to use the SDL method for long-duration GW searches like that for EMRIs in this study.

gr-qc↗

arXiv2024

$I$-Love-$Q$, and $δM$ too: The role of the mass in universal relations of compact stars

In the study of rotating neutron stars the $I$-Love-$Q$ relations refer to the existence of various approximate, equation of state-independent relations involving the moment of inertia, the Love number and the quadrupole moment. These relations are relevant for observational astrophysics, since they allow (in theory) the inference of any two quantities within the $I$-Love-$Q$ triad out of the third one alone. However, the quantities involved in the relations are, in fact, normalized by a parameter $M_0$ that arises in the usual perturbative analytical approach as the mass of the background configuration. Since $M_0$ is not the mass of the rotating star $M_S$, it is not an observational quantity, which may affect the application of the relations to actual observations. This situation is usually ignored in most studies by taking $M_0$ to be the mass of the star, an approximation that can, in some cases, be inconsistent. In this paper we extract the value of $M_0$ using an $\textit{extended}$ version of the universal relations that involve a fourth parameter, $δM$, proportional to the difference $M_S-M_0$. We analyze to which degree this extended set of relations yields a more precise inference of compact star properties and equation of state parameters.

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arXiv2024

Quasinormal Modes in Modified Gravity using Physics-Informed Neural Networks

In this paper, we apply a novel approach based on physics-informed neural networks to the computation of quasinormal modes of black hole solutions in modified gravity. In particular, we focus on the case of Einstein-scalar-Gauss-Bonnet theory, with several choices of the coupling function between the scalar field and the Gauss-Bonnet invariant. This type of calculation introduces a number of challenges with respect to the case of General Relativity, mainly due to the extra complexity of the perturbation equations and to the fact that the background solution is known only numerically. The solution of these perturbation equations typically requires sophisticated numerical techniques that are not easy to develop in computational codes. We show that physics-informed neural networks have an accuracy which is comparable to traditional numerical methods in the case of numerical backgrounds, while being very simple to implement. Additionally, the use of GPU parallelization is straightforward thanks to the use of standard machine learning environments.

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arXiv2024

Numerical relativity surrogate models for exotic compact objects: the case of head-on mergers of equal-mass Proca stars

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 $ψ_4$. 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.

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