arXiv subjects

Nikolaos Stergioulas

Publications and source records attributed to Nikolaos Stergioulas.

At least 19 recordsLinked to original sources

arXiv2026

Rotating Neutron Star Migrations as a Standardized Test for 3+1 Numerical Relativity

When simulating dynamically unstable neutron stars, truncation errors can introduce perturbations that drive the stellar configuration either toward gravitational collapse into a black hole or toward migration to a dynamically stable, lower-density configuration. This mechanism has become a standard benchmark for validating general-relativistic hydrodynamics codes in the case of spherically symmetric neutron-star models. In this work, we extend the analysis to the more general case of uniformly rotating neutron stars evolved in three spatial dimensions. We investigate these transitions using two equations of state: a cold polytrope and a hybrid prescription, which allow us to distinguish the barotropic response from the effects of shock-generated thermal pressure. We find that the migrated remnant remains close to uniform rotation after the transition, and we trace its quasi-radial pulsations through complementary diagnostics involving the central density, the rotation profile, and the axisymmetric gravitational-wave channel. We investigate the properties of these transitions and propose a standardized test for validating numerical-relativity codes by requiring the migration behavior to be consistent with the dynamics reported here.

gr-qc↗

arXiv2026

Learned proposals in trans-dimensional inference are optimal at equilibrium, not during assembly

Inferring the dimension of a model - the number of components needed to explain data - jointly with the parameters is a pervasive problem, from counting sources in an image to mixture modeling, and reversible-jump Markov chain Monte Carlo solves it exactly but mixes slowly. Learned proposals are well established at fixed dimension, but whether they can accelerate the dimension-changing moves themselves has remained largely untested. We show that the answer has a structural origin: the optimal proposal for the dimension-changing birth move is a different object in different phases of the run. While the fit is being assembled it must match the current residual - a state-dependent quantity no state-independent network can represent - but at equilibrium it degenerates to the posterior's single-component marginal, which is exactly the distribution an adaptive normalizing flow learns from the sampler's own history. A learned state-independent birth proposal is therefore useless in one phase and optimal in the other. Controlled experiments confirm the attribution: applied with an exact Metropolis--Hastings correction that leaves the target invariant for any network, the learned births leave acceptance rates unchanged yet accelerate model-order mixing - in a ten-seed benchmark they meet a pre-specified stopping rule in six of ten runs, typically several times sooner, where a strong hand-tuned baseline meets it in one (one-sided p=0.03) - and an isolation experiment shows the same flow deployed within-model buys nothing. Making no domain-specific assumptions, the same sampler counts sources in a noisy image and reconstructs signals across scientific domains, including gravitational waves from ground- and space-based detectors and a scalp EEG recording. We release the method as HyperWave, an open-source package.

physics.data-an↗

arXiv2026

Inferring Neutron-Star Properties from Post-merger Gravitational-wave Spectra with Neural Networks

We present a proof-of-concept study of the inverse problem of inferring neutron-star properties directly from the post-merger gravitational-wave spectrum of equal-mass binary neutron-star mergers. Using noise-free spectra from numerical-relativity catalogs, we train and compare three artificial-neural-network regression models and two multivariate linear-regression baselines to predict the stellar mass, $M$, the quadrupolar tidal deformability, $κ_2^τ$, and the slope of the mass--radius relation, $dR/dM$. Since the inverse mapping is nonlinear and cannot be obtained by analytically inverting the direct neural-network model, we construct inverse surrogates and train the networks with a two-stage procedure in which residuals from an initial pass define sample weights for a second pass, together with regularization via dropout, Gaussian-noise injection, and early stopping. We find that neural networks consistently outperform linear baselines, showing that nonlinear surrogates capture the inverse relation between post-merger spectra and source properties more effectively than algebraic inversion. The best performance is achieved by an ensemble of single-task networks, while a multi-task model gives comparable accuracy for predicting the mass--radius slope, and a mixture-of-experts architecture provides insight into spectral-region importance. We further show that the best model reproduces empirical relations between the dominant post-merger frequency and tidal deformability, and recovers equation-of-state-dependent mass--tidal-deformability trends, indicating physical consistency beyond pointwise accuracy. Although restricted to idealized noise-free spectra, the results show that neural-network surrogates provide a promising route for extracting neutron-star information from post-merger signals with future third-generation detectors.

gr-qc↗

arXiv2026

Low-$T/|W|$ instabilities in differentially rotating neutron stars resembling merger remnants

We construct constant rest-mass sequences of equilibrium models of differentially rotating neutron stars which resemble binary neutron star post-merger remnants. For a more realistic description of the post-merger remnant, we impose that each model carries approximately $95\%$ of the angular momentum that a binary system with the same total rest-mass has at the moment of merging, based on an empirical relation informed from neutron star merger simulations. We account for equation of state effects by employing two distinct microphysical descriptions for high density matter. We dynamically evolve the equilibrium models with a three-dimensional general relativistic hydrodynamics code that employs the conformal flatness approximation. We investigate the connection between the occurrence of the instability and the existence of corotation radii within the stellar configurations and determine the instability window for both equation of state sequences. The occurrence of low-$T/|W|$ instabilities leads to pronounced gravitational wave emission in the range $0.13 \lessapproxβ\lessapprox 0.2$, while models outside this range exhibit less pronounced features in the gravitational wave spectrum. The prominence of gravitational wave emission is primarily determined by $β$, while the equation of state seems to have a more minor effect. We present correlations between the strength of the gravitational wave emission associated with the instability and properties of the equilibrium models. Stellar configurations modelled by different equations of state display differences in the timescales over which the various dynamical features develop, as well as whether they exhibit a pronounced $m=1$ deformation. Potential relations between the instability growth timescales and properties of the stellar models are studied.

astro-ph.HE↗

arXiv2026

Beyond Gaussian Assumptions: A new robust statistical framework for gravitational-wave data analysis

Many traditional algorithms applied in gravitational-wave astronomy rely on the assumption of Gaussian noise, a condition not always met. To meet this need, this study extends a robust statistical framework, advancing previous work on heavy-tailed likelihoods, that adapts the hyperbolic likelihood method for full frequency domain applications. The framework is designed to maintain high performance under ideal conditions while improving robustness against non-Gaussian noise and outliers in real-world data. We demonstrate the efficacy of this approach through two key case studies. The first case study analyzes a massive black hole binary merger in simulated Laser Interferometer Space Antenna (LISA) data with Gaussian noise, showing that the extended hyperbolic likelihood method performs comparably to the more commonly used Whittle likelihood. The second case study examines a stellar-mass black hole binary merger using real ground-based gravitational-wave data containing non-Gaussian noise or overlapping signals, where our framework exhibits increased robustness and yields more accurate parameter estimations. Our results show that the hyperbolic likelihood better captures the true noise distribution, providing a flexible and physically motivated alternative for GW data analysis across current and future detectors.

gr-qc↗

arXiv2026

GW-FALCON: A Novel Feature-Driven Deep Learning Approach for Early Warning Alerts of BNS and NSBH Inspirals in Next-Generation GW Observatories

Next-generation GW observatories such as the ET and CE will detect BNS and NSBH inspirals with high SNRs and long in-band durations, making systematic early-warning alerts both feasible and scientifically valuable. Such triggers are essential for coordinating rapid electromagnetic follow-up. In this work, we introduce GW-FALCON, a novel feature-driven DL framework for early-time detection between GW signal+noise and noise-only data in next-generation detectors. Instead of feeding raw time series to CNN or more complex neural network architectures, we first extract a large set of statistical, temporal, and spectral quantities from short observational time windows using the TSFEL library. The resulting fixed-length feature vectors are then used as input to feed-forward ANNs suitable for low-latency operation. We demonstrate the method using simulated BNS and NSBH inspiral waveforms injected into colored Gaussian noise generated from the ET and CE design PSDs. We train separate ANNs on feature sets extracted from partial-inspiral windows characterized by different maximum instantaneous frequencies, enabling early-warning triggers from tens to hundreds of seconds before merger. Across all detector configurations and datasets, the resulting classifiers achieve high accuracy and detection efficiency, with ET-like networks typically reaching test accuracies of order 90% and CE-like ones exceeding 97% at low false-alarm probability. To the best of our knowledge, this work presents the first comprehensive feature-based DL detection framework for Next-generation GW observatories, connecting feature extraction from strain time series data to robust signal-noise classification within a setup that can be extended to real data and to more advanced neural network architectures.

astro-ph.IM↗

arXiv2026

ESO Expanding Horizon White Paper: Revealing the properties of matter at supranuclear densities with gravitational waves

Understanding dense matter under extreme conditions is one of the most fundamental puzzles in modern physics. Complex interactions give rise to emergent, collective phenomena. While nuclear experiments and Earth - based colliders provide valuable insights, much of the quantum chromodynamics phase diagram at high density and low temperature remains accessible only through astrophysical observations of neutron stars, neutron star mergers, and stellar collapse. Astronomical observations thus offer a direct window to the physics on subatomic scales with gravitational waves presenting an especially clean channel. Next-generation gravitational - wave observatories, such as the Einstein Telescope, would serve as unparalleled instruments to transform our understanding of neutron star matter. They will enable the detection of up to tens of thousands of binary neutron star and neutron star - black hole mergers per year, a dramatic increase over the few events accessible with current detectors. They will provide an unprecedented precision in probing cold, dense matter during the binary inspiral, exceeding by at least an order of magnitude what current facilities can achieve. Moreover, these observatories will allow us to explore uncharted regimes of dense matter at finite temperatures produced in a subset of neutron star mergers, areas that remain entirely inaccessible to current instruments. Together with multimessenger observations, these measurements will significantly deepen our knowledge of dense nuclear matter.

astro-ph.IM↗

arXiv2025

Assessing Universal Relations for Rapidly Rotating Neutron Stars: Insights from an Interpretable Deep Learning Perspective

Relations between stellar properties independent of the nuclear equation of state offer profound insights into neutron star physics and have practical applications in data analysis. Commonly, these relations are derived from utilizing various realistic nuclear cold hadronic, hyperonic, and hybrid EoS models, each of which should obey the current constraints and cover a wide range of stiffnesses. Concurrently, the field of multimessenger astronomy has been significantly enhanced by the advent of gravitational wave astronomy, which increasingly incorporates deep learning techniques and algorithms. At the same time, X-ray spectral data from NICER based on known pulsars are available, and additional observations are expected from upcoming missions. In this study, we revisit established universal relations, introduce new ones, and reassess them using a feed-forward neural network as a regression model. More specifically, we mainly propose ``deep'' EoS-insensitive hypersurface relations for rapidly rotating compact objects between several of the star's global parameters, which achieve an accuracy of within $1\%$ in most cases, with only a small fraction of investigated models exceeding this threshold. While analytical expressions can be used to represent some of these relations, the neural network approach demonstrates superior performance, particularly in complex regions of the parameter space. Furthermore, we use the SHapley Additive exPlanations (SHAP) method to interpret the suggested network's predictions, since is based on a strong theoretical framework inspired by the field of cooperative Game Theory. Most importantly, these highly accurate universal relations empowered with the interpretability description could be used in efforts to constrain the high-density equation of state in neutron stars, with the potential to enhance our understanding as new observables emerge.

astro-ph.HE↗

arXiv2025

Robustness of Sensitivity Evaluations for Gravitational Wave Detection Algorithms

The discovery of gravitational waves (GWs) from merging compact binaries has transformed modern astrophysics, driving innovation in detection methodologies. Whereas matched-filtering techniques have long been the standard, the growing volume of data from advanced observatories like LIGO, Virgo, and KAGRA has spurred interest in machine learning (ML) solutions for their scalability and computational efficiency. As next-generation detectors approach reality, the development of reliable and adaptable search algorithms becomes increasingly urgent. This work examines the consistency of detection sensitivity in AresGW model 1, an ML-based pipeline, when applied to multiple month-long datasets consisting of real detector noise. By analyzing the number of waveform injections detected and the sensitive distance at different FAR thresholds, we evaluate the model's performance under low false alarm rates and investigate how sensitivity metrics fluctuate due to dataset variability. In addition, we evaluate the performance of our algorithm on data both with and without contamination of genuine GW signals. Our findings reveal notable performance variations, highlighting the challenges introduced by finite-duration datasets and emphasizing the need for more rigorous statistical validation. By identifying these challenges, we aim to clarify the practical limitations of both ML-based and traditional detection systems and inform future benchmarking standards for GW searches.

gr-qc↗

arXiv2025

Gravitational-wave model for neutron star merger remnants with supervised learning

We present a time-domain model for the gravitational waves emitted by equal-mass binary neutron star merger remnants for a fixed equation of state. We construct a large set of numerical relativity simulations for a single equation of state consistent with current constraints, totaling 157 equal-mass binary neutron star merger configurations. The gravitational-wave model is constructed using the supervised learning method of K-nearest neighbor regression. As a first step toward developing a general model with supervised learning methods that accounts for the dependencies on equation of state and the binary masses of the system, we explore the impact of the size of the dataset on the model. We assess the accuracy of the model for a varied dataset size and number density in total binary mass. Specifically, we consider five training sets of $\{ 20,40, 60, 80, 100\}$ simulations uniformly distributed in total binary mass. We evaluate the resulting models in terms of faithfulness using a test set of 30 additional simulations that are not used during training and which are equidistantly spaced in total binary mass. The models achieve faithfulness with maximum values in the range of $0.980$ to $0.995$. We assess our models simulating signals observed by the three-detector network of Advanced LIGO-Virgo. We find that all models with training sets of size equal to or larger than $40$ achieve an unbiased measurement of the main gravitational-wave frequency. We confirm that our results do not depend qualitatively on the choice of the (fixed) equation of state. We conclude that training sets, with a minimum size of $40$ simulations, or a number density of approximately $11$ simulations per $0.1\,M_\odot$ of total binary mass, suffice for the construction of faithful templates for the post-merger signal for a single equation of state and equal-mass binaries (abbreviated).

astro-ph.HE↗

arXiv2025

Long-Baseline Atom Interferometry

Long-baseline atom interferometry is a promising technique for probing various aspects of fundamental physics, astrophysics and cosmology, including searches for ultralight dark matter (ULDM) and for gravitational waves (GWs) in the frequency range around 1~Hz that is not covered by present and planned detectors using laser interferometry. The MAGIS detector is under construction at Fermilab, as is the MIGA detector in France. The PX46 access shaft to the LHC has been identified as a very suitable site for an atom interferometer of height $\sim 100$m, sites at the Boulby mine in the UK and the Canfranc Laboratory are also under investigation, and possible sites for km-class detectors have been suggested. The Terrestrial Very-Long-Baseline Atom Interferometry (TVLBAI) Proto-Collaboration proposes a coordinated programme of interferometers of increasing baselines.

hep-ex↗

arXiv2025

Universal description of the Neutron Star's surface and its key global properties: A Machine Learning Approach for nonrotating and rapidly rotating stellar models

Neutron stars provide an ideal theoretical framework for exploring fundamental physics when nuclear matter surpasses densities encountered within atomic nuclei. Despite their paramount importance, uncertainties in the equation of state (EoS) have shrouded their internal structure. For rotating neutron stars, the shape of their surface is contingent upon the EoS and the rotational dynamics. This work proposes new universal relations regarding the star's surface, employing machine-learning techniques for regression. More specifically, we developed highly accurate universal relations for a neutron star's eccentricity, the star's ratio of the polar to the equatorial radius, and the effective gravitational acceleration at both the pole and the equator. Furthermore, we propose an accurate theoretical formula for $(d\log R(μ)/dθ)_{\max}$. This research addresses key astronomical aspects by utilizing these global parameters as features for the training phase of a neural network. Along the way, we introduce new effective parameterizations for each star's global surface characteristics. Our regression methodology enables accurate estimations of the star's surface $R(μ)$, its corresponding logarithmic derivative $d\log R(μ)/dθ$, and its effective acceleration due to gravity $g(μ)$ with accuracy better than $1 \%$. The analysis is performed for an extended sample of rotating configurations constructed using a large ensemble of 70 tabulated hadronic, hyperonic, and hybrid EoS models that obey the current multimessenger constraints and cover a wide range of stiffnesses. Above all, the suggested relations could provide an accurate framework for the star's surface estimation using data acquired from the NICER X-ray telescope or future missions, and constrain the EoS of nuclear matter when measurements of the relevant observables become available.

astro-ph.HE↗

arXiv2025

Robust and fast parameter estimation for gravitational waves from binary neutron star merger remnants

We present a robust and efficient methodology for parameter estimation of gravitational waves generated during the post-merger phase of binary neutron star mergers. Our approach leverages an analytic waveform model combined with empirical relations to predict prior ranges for the post-merger frequencies based on measurements of the chirp mass and effective tidal deformability in the inspiral phase. This enables robust inference of the main features of the post-merger spectrum, avoiding possible multi-modality induced by wide priors. Using waveforms derived from numerical relativity, we systematically validate our model across a broad spectrum of neutron star equations of state and mass configurations, demonstrating high fitting factors. Our method can be applied in future detections of gravitational waves from the post-merger phase with third-generation gravitational wave observatories. Furthermore, by integrating the Preconditioned Monte Carlo sampling method within the pocoMC framework, we achieve substantial computational acceleration compared to conventional Bayesian techniques.

gr-qc↗

arXiv2024

An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

The large-scale analysis task of deciphering gravitational wave signals in the LISA data stream will be difficult, requiring a large amount of computational resources and extensive development of computational methods. Its high dimensionality, multiple model types, and complicated noise profile require a global fit to all parameters and input models simultaneously. In this work, we detail our global fit algorithm, called ``Erebor,'' designed to accomplish this challenging task. It is capable of analysing current state-of-the-art datasets and then growing into the future as more pieces of the pipeline are completed and added. We describe our pipeline strategy, the algorithmic setup, and the results from our analysis of the LDC2A Sangria dataset, which contains Massive Black Hole Binaries, compact Galactic Binaries, and a parameterized noise spectrum whose parameters are unknown to the user. The Erebor algorithm includes three unique and very useful contributions: GPU acceleration for enhanced computational efficiency; ensemble MCMC sampling with multiple MCMC walkers per temperature for better mixing and parallelized sample creation; and special online updates to reversible-jump (or trans-dimensional) sampling distributions to ensure sampler mixing and accurate initial estimates for detectable sources in the data. We recover posterior distributions for all 15 (6) of the injected MBHBs in the LDC2A training (hidden) dataset. We catalog $\sim12000$ Galactic Binaries ($\sim8000$ as high confidence detections) for both the training and hidden datasets. All of the sources and their posterior distributions are provided in publicly available catalogs.

gr-qc↗

arXiv2024

Characterization of non-Gaussian stochastic signals with heavier-tailed likelihoods

Future Gravitational Wave observatories will give us the opportunity to search for stochastic signals of astrophysical, or even cosmological origins. However, parameter estimation and search will be challenging, mostly due to the overlap of multiple signal components, as well as the potentially partially unknown properties of the instrumental noise. In this work, we propose a robust statistical framework based on heavier-tailed likelihoods for the characterization of stochastic gravitational-wave signals. In particular, we use the symmetric hyperbolic likelihood, which allows us to probe the signal spectral properties and simultaneously test for any departures from Gaussianity. We demonstrate this methodology with synthetic data from the future LISA mission, where we estimate the potential non-Gaussianities induced by the unresolved Ultra Compact Galactic Binaries.

gr-qc↗

arXiv2024

New Gravitational Wave Discoveries Enabled by Machine Learning

The detection of gravitational waves has revolutionized our understanding of the universe, offering unprecedented insights into its dynamics. A major goal of gravitational wave data analysis is to speed up the detection and parameter estimation process using machine learning techniques, in light of an anticipated surge in detected events that would render traditional methods impractical. Here, we present the first detections of new gravitational-wave candidate events in data from a network of interferometric detectors enabled by machine learning. We discuss several new enhancements of our ResNet-based deep learning code, AresGW, that increased its sensitivity, including a new hierarchical classification of triggers, based on different noise and frequency filters. The enhancements resulted in a significant reduction in the false alarm rate, allowing AresGW to surpass traditional pipelines in the number of detected events in its effective training range (single source masses between 7 and 50 solar masses and source chirp masses between 10 and 40 solar masses), when the new detections are included. We calculate the astrophysical significance of events detected with AresGW using a logarithmic ranking statistic and injections into O3 data. Furthermore, we present spectrograms, parameter estimation, and reconstruction in the time domain for our new candidate events and discuss the distribution of their properties. In addition, the AresGW code exhibited very good performance when tested across various two-detector setups and on observational data from the O1 and O2 observing periods. Our findings underscore the remarkable potential of AresGW as a fast and sensitive detection algorithm for gravitational-wave astronomy, paving the way for a larger number of future discoveries.

gr-qc↗

arXiv2024

Predicting Binary Neutron Star Postmerger Spectra Using Artificial Neural Networks

Gravitational waves in the postmerger phase of binary neutron star mergers may become detectable with planned upgrades of existing gravitational-wave detectors or with more sensitive next-generation detectors. The construction of template banks for the postmerger phase can facilitate signal detection and parameter estimation. Here, we investigate the performance of an artificial neural network in predicting simulation-based waveforms in the frequency domain (restricted to the magnitude of the frequency spectrum and to equal-mass models) that depend on three parameters that can be inferred through observations, neutron star mass, tidal deformability, and the gradient of radius versus mass. Compared to a baseline study using multiple linear regression, we find that the artificial neural network can predict waveforms with higher accuracy and more consistent performance in a cross-validation study. We also demonstrate, through a recalibration procedure, that future reduction of uncertainties in empirical relations that are used in our hierarchical scheme will result in more accurate predicted postmerger spectra.

gr-qc↗

arXiv2024

Machine Learning Applications in Gravitational Wave Astronomy

Gravitational wave astronomy has emerged as a new branch of observational astronomy, since the first detection of gravitational waves in 2015. The current number of $O(100)$ detections is expected to grow by several orders of magnitude over the next two decades. As a result, current computationally expensive detection algorithms will become impractical. A solution to this problem, which has been explored in the last years, is the application of machine-learning techniques to accelerate the detection and parameter estimation of gravitational wave sources. In this chapter, several different applications are summarized, including the application of artificial neural networks and autoenconders in accelerating the computation of surrogate models, deep residual networks in achieving rapid detections with high sensitivity, as well as artificial neural networks for accelerating the construction of neutron star models in an alternative theory of gravity.

gr-qc↗