arXiv subjects

Katerina Chatziioannou

Publications and source records attributed to Katerina Chatziioannou.

At least 19 recordsLinked to original sources

arXiv2026

Toward a complete understanding of the properties of GW190521

Interpreting GW190521 remains challenging due to its exceptionally high total mass and correspondingly small number of observable gravitational-wave cycles, which gives rise to degeneracies between different physical interpretations. Motivated by the outstanding questions surrounding the event, we present a comprehensive reanalysis of GW190521 using waveform models drawn from multiple families, including a model that includes both eccentricity and spin-precession. We find evidence that the source was highly eccentric, with an inferred eccentricity of $0.81^{+0.05}_{-0.01}$ at 5 Hz, and show that non-eccentric interpretations arise from failing to probe the high-likelihood region at large eccentricities. We further demonstrate that when eccentricity is included in the analysis, there is no indication of spin precession, suggesting that the apparent precession in quasicircular analyses is at least partially driven by an eccentricity--spin-precession degeneracy. While all waveform models favor a comparable-mass binary, one model admits a secondary mode at more asymmetric masses. We find a robust probability $(\geq97\%)$ that the primary black hole lies within the pair-instability supernova mass gap ($\sim60-130\,M_\odot$), whereas the probability that the secondary lies in the gap depends on the waveform model. Through inference of GW190521-like injections, we show that the waveform systematics observed in the analysis of GW190521 could arise from the response of models to unmodeled eccentricity, rather than from intrinsic differences between waveform models. When analyzing a quasicircular spin-precessing injection similar to GW190521, we find no significant model dependence in the inferred parameters. Finally, because the inferred eccentricity lies beyond the calibration regime of current models, we test its robustness by recovering highly eccentric numerical-relativity waveforms.

gr-qc↗

arXiv2026

Observational selection effects on radio pulsars are minimal for masses, but significant for orbits and spins

The masses of Galactic pulsars in binaries measured through radio timing point to structure in the mass distribution with implications for dense matter and astrophysical formation processes: a bimodal shape with peaks at ${\sim} 1.3\,M_\odot$ and ${\sim} 1.6\,M_\odot$, with a cutoff above ${\sim}2\,M_\odot$. However, this observed population of radio pulsars is shaped not only by intrinsic pulsar properties, such as birth masses, binary evolutionary pathways and dense-matter constraints, but also by observational effects. In existing catalogs of pulsar mass measurements, observational selection effects influence (i) which systems are detected in the first place (detectability) and (ii) which systems yield well-measured masses (measurability). In this work, we revisit the radio pulsar population distribution in the context of observational effects associated with pulsar radio timing. Simulating systems of radio pulsars and timing observations, we track the process by which a given system ends up in an observational dataset and estimate the impact of the binary and pulsar parameters. We fold this selection function into hierarchical population inference and extract the intrinsic astrophysical distribution of pulsars observed with radio timing. We jointly infer the populations of pulsar mass, companion mass, orbital eccentricity, orbital period and pulsar spin period. Selection effects have minimal impact on the mass distributions, but more significantly affect other parameters. The ratio of circular-to-eccentric systems shifts from an observed $1\!:\!1$ to an astrophysical, selection-corrected $2\!:\!1$ ratio, while the true population is shifted toward longer orbital and spin periods than observed.

astro-ph.HE↗

arXiv2026

labrador: A domain-optimized machine-learning tool for gravitational wave inference

Fast and reliable inference of gravitational-wave source parameters is crucial for analyzing large catalogs that are reaching the size of hundreds of detections, and for identifying short-lived electromagnetic counterparts. Neural posterior estimation has emerged as a powerful inference method, where the model is trained on simulated gravitational-wave data at considerable computational cost, but thereafter enables extremely fast and inexpensive inference at test time. Here, we extend this approach by incorporating domain-specific physical insights and methods in the model architecture. These include compressing the data by heterodyning against a reference waveform chosen via approximate likelihood maximization, removing parameter degeneracies through tailored coordinate systems, and eliminating known multimodalities by folding the parameter space. As a result, the network is approximately equivariant to changes in the source parameters, and achieves a reduced training cost and improved model interpretability. Our implementation, called labrador, can be trained end-to-end on a 1-day timescale on $\sim 10^2$ CPU cores and a V100 GPU, achieving a median importance-sampling efficiency of 1% on quadrupolar, aligned-spin signals in a broad mass range (chirp mass $\mathcal{M} \in 1\text{-}50\,\mathrm{M}_\odot$, mass ratio $q > 0.1$). labrador is the first neural inference code to achieve extensive coverage of long-duration signals with secondary masses $m_2 < 10\,\mathrm{M}_\odot$, rendered possible by its equivariance property. Among our novel contributions is a numerically stable procedure that enables neural posterior estimation when the simulation and inference priors differ.

gr-qc↗

arXiv2026

The WDM Time-Frequency Transform in Gravitational-Wave Data Analysis I: Formalism

For slowly-varying noise, time-frequency methods offer a natural middle ground between the efficiency of the frequency domain and the generality of the more expensive time domain. Despite growing adoption, such methods remain less well documented and less familiar in the gravitational-wave literature, compared to the ubiquitous frequency domain. Aimed at gravitational-wave analysts, in this paper we present a self-contained account of time-frequency methods, detail derivations for a specific basis, namely the Wilson-Daubechies-Meyer (WDM) basis, and share intuition and lessons learned. We document key concepts: the orthogonality and good time-frequency localization of the basis, the edge effects at the DC and Nyquist frequencies, the forward and inverse transforms and their practical implementation, and the noise covariance matrix and likelihood in the time-frequency domain.

gr-qc↗

arXiv2026

A comprehensive look into the accuracy of SpEC binary black hole waveforms

Numerical relativity simulations provide a full description of the dynamics of binary systems, including gravitational radiation. The waveforms produced by these simulations have a number of applications in gravitational-wave detection and inference. In this work, we revisit the accuracy of the waveforms produced by the Spectral Einstein Code. Motivated by the wide range of waveform applications, we propose and explore three accuracy metrics between simulation resolutions: (i) the generalized frequency-weighted mismatch, (ii) the relative amplitude difference, and (iii) the phase difference at different times. We confirm that numerical errors accumulate over the binary evolution, but the error is not intrinsically larger during the latest, more dynamical stages. Studying errors across the parameter space, we identify a positive correlation between both the mismatch and the phase difference with precessing spin, but little correlation with aligned spin or eccentricity. Lastly, amplitude and phases differences are symmetric upon exchanging resolutions across the catalog, suggesting that the dominant source of error is random, rather than something systematic that affects all waveforms similarly.

gr-qc↗

arXiv2026

Photon counting readout for detection and inference of gravitational waves from neutron star merger remnants

Gravitational waves emitted after neutron star binary coalescences and the information they carry about dense matter are a high-priority target for next-generation detectors. Even though such detectors are expected to observe millions of signals, detectable postmerger emission will remain rare. In this work, we explore postmerger detectability and inference through an alternative detector readout scheme for data dominated by quantum-noise, which is the case above $1$\,kHz: photon-counting. In such a readout, signals and noise become quantized into discrete distributions corresponding to the detection of single photons measured in a chosen basis of modes. Through simulated data, we demonstrate that photon counting can be efficient even for weak signals. We find ${\sim}1$ in 100 signals with a postmerger signal-to-noise ratio of 0.2 can result in a single photon and thus be detected. Furthermore, after $2\times10^4$ signals -- equivalent to $10^{-2}$ to $1.5$ years of observation -- photon counting results in a twofold improvement in the measurement of the radius of a $1.6\,M_\odot$ neutron star. Constraints can be further tightened if the detector classical noise is reduced. Photon counting offers a promising alternative to traditional homodyne readout techniques for extracting information from low signal-to-noise ratio postmerger signals.

gr-qc↗

arXiv2026

Coalescing Compact Binary Parameter Estimation with Gravitational Waves in the Presence of non-Gaussian Transient Noise

Data from gravitational-wave (GW) detectors often contains a high rate of non-Gaussian transient noise, known as glitches. The parameters estimated from GW signals coinciding with detector glitches are occasionally biased away from their true values. During the first part of the fourth LIGO-Virgo-KAGRA (LVK) observing run, 29% of GW candidates had overlapping or nearby glitches in one or more detectors. In the latter part of the fourth observation run, sensitivity improvements have increased the rates of GW detection. Consequently, scenarios in which GW signals and detector glitches overlap in time are more likely. In this study, we quantify shifts in inferred posterior distributions for short-duration compact binary coalescence GW signals interacting with common LIGO glitches as a function of time between the signal merger time and the glitch. We find statistically significant biases in parameter estimation for mass, spin, and sky position for "blip", "thunder", and "fast-scattering" glitches. Using these results, we provide estimates of what parameters are most affected by overlapping noise sources, as well as what constitutes a "safe" time separation between a gravitational wave signal and a glitch, without requiring glitch subtraction for unbiased source property estimation. We find that in a majority of cases, all parameters are susceptible to significant bias due to glitch interference. Additionally, we find that glitches that occur within the time prior of the GW signal cause more extreme biases than glitches outside of the time prior.

gr-qc↗

arXiv2026

Posterior Predictive Checks for Gravitational-wave Populations: Limitations and Improvements

When selecting a model to characterize an astrophysical population, it is crucial to assess whether that model fits the data and, if not, how it can be improved. To this end, posterior predictive checks (PPCs) are a widely-used statistical test of model fit when inferring gravitational-wave source populations. However, PPCs exhibit limitations when assessing single-event parameters with large measurement uncertainty, like the spin tilt angles of the binary black holes (BBHs) observable with the LIGO-Virgo-KAGRA (LVK) detectors. When single-event inference is prior-dominated, traditional PPCs fail to flag even very poor model fits. In this work, we assess the efficacy of various alternative PPCs on poorly-constrained parameters. We compare PPCs conducted on event- vs. data-level parameters (e.g. posterior samples vs. maximum likelihood points), and explore two additional event-level PPCs: partial predictive checks and split predictive checks. Independent of measurement uncertainty, we find that PPCs on maximum likelihood parameters are always more discerning of model misspecification than any event-level PPC. However, when investigating simulated GWTC-3.0-like catalogs, none of the alternative PPCs show significant improvement over those traditionally used, indicating that at that sensitivity, any limited information in the data about spin tilts is insufficient to diagnose model misspecification. Finally, we apply our suite of PPCs to the spin magnitude and tilt distributions inferred in the most recent LVK catalog, GWTC-4.0. We conclude that the Gaussian Component Spins model used therein under-predicts BBHs with large spin magnitudes and over-predicts those with perfectly anti-aligned tilts.

gr-qc↗

arXiv2026

Low-mass failed supernovae and the $10\,M_{\odot}$ peak in the merging black hole mass distribution

Gravitational-wave observations reveal that the rate of merging black holes drops by $\sim2$ orders of magnitude from component masses $\sim 10\,M_{\odot}$ to $\sim 15\,M_{\odot}$. The increased compactness of the black hole progenitor cores may contribute to the $\sim 10\,M_{\odot}$ overdensity, but cannot fully explain the rate difference. In this paper, we consider the possibility that the overdensity is reinforced by supernova processes that result in efficient black hole formation from direct collapse in a narrow range around $10\, M_{\odot}$. We extend previous studies by considering a distinct subpopulation of failed-supernovae black holes, possibly separated by a gap in the primary mass distribution from the rest of the population. Using 153 observations from the latest GWTC-4.0 catalog, we confirm a strong peak in the primary mass distribution at $10\,M_{\odot}$, with a peak rate density of $7.36_{-3.11}^{+6.35}$ $M_{\odot}^{-1}\mathrm{yr}^{-1} \mathrm{Gpc}^{-3}$. The rate drops sharply and becomes consistent with zero at the 90 % level for primary mass $m_1\in (12.0, 16.1)\, M_{\odot}$, then rises again to confidently nonzero values above $\sim 16\,M_{\odot}$ before falling at higher masses. Our results reveal structure in the mass distribution in the $10-20\,M_{\odot}$ range, with rate changes of multiple orders of magnitude across a few solar masses, consistent with a distinct population of failed-supernova black holes.

astro-ph.HE↗

arXiv2026

Revisiting the Coprecessing Frame in the Presence of Orbital Eccentricity

Accurate inclusion of both spin precession and orbital eccentricity effects in gravitational waveform models represents a key hurdle in our ability to fully characterize the properties of compact binaries. Virtually all efforts to model precession rely on a coprecessing frame transformation, a time-dependent spatial rotation that tracks the dominant emission direction and simplifies the waveform morphology. We assess the utility of the coprecessing frame transformation to separate out the effect of the precession of the orbital plane from the waveform in the presence of non-negligible orbital eccentricity. We rely on 20 numerical relativity simulations, which include the complete physical effects of spin precession and eccentricity in the strong-field, and compare waveforms in both the inertial and coprecessing frames. Comparing against the eccentric, spin-aligned model SEOBNRv5EHM, we find that while the waveform mismatches decrease in the coprecessing frame, they remain above the level required for accurate waveform modeling, $\sim$ 0.01 or higher for large inclinations. Further improvements, e.g., modeling mode asymmetries as already pursued for quasicircular binaries, will likely prove essential. We also find that by removing the dominant amplitude and phase modulations from the waveform, the coprecessing frame facilitates surrogate modeling, achieving lower errors at a fixed number of basis elements compared to the inertial frame. Our results demonstrate both the utility and the limitations of the coprecessing frame as a cornerstone in waveform modeling for eccentric and precessing binaries.

gr-qc↗

arXiv2026

Inferring the spins of merging black holes in the presence of data-quality issues

Gravitational waves from black hole binary mergers carry information about the component spins, but inference is sensitive to analysis assumptions, which may be broken by terrestrial noise transients known as glitches. Using a variety of simulated glitches and gravitational wave signals, we study the conditions under which glitches can bias spin measurements. We confirm the theoretical expectation that inference and subtraction of glitches invariably leaves behind residual power due to statistical uncertainty, no matter the strength (signal-to-noise ratio; SNR) of the original glitch. Next we show that low-SNR glitches - including those below the threshold for flagging data-quality issues - can still significantly bias spin inference. Such biases occur for a range of glitch morphologies, even in cases where glitches and signals are not precisely aligned in phase. Furthermore, we find that residuals of glitch subtraction can result in biases as well. Our results suggest that joint inference of the glitch and gravitational wave parameters, with appropriate models and priors, is required to address these uncertainties inherent in glitch mitigation via subtraction.

gr-qc↗

arXiv2026

The impact of waveform systematics and Gaussian noise on the interpretation of GW231123

GW231123 is an exceptional gravitational-wave event consistent with the merger of two massive, highly-spinning black holes. Reliable inference of the source properties is crucial for accurate interpretation of its astrophysical implications. However, characterization of GW231123 is challenging: only few signal cycles are observed and different signal models result in systematically different parameters. We investigate whether the interpretation of GW231123 is robust against model systematics and Gaussian detector noise. We show that the model systematics observed in GW231123 can be reproduced for a simulated signal based on the numerical-relativity surrogate model NRSur7dq4. Simulating data using the maximum-likelihood NRSur7dq4 waveform for GW231123 and no noise realization, we closely recover the systematics observed for the real signal. We then explore how the headline properties of GW231123 are impacted by Gaussian detector noise. Using the NRSur7dq4 maximum-likelihood waveform and different noise realizations, we consistently find support for large masses, high spin magnitudes (median $χ_1\geq 0.7$), and high spin precession (median $χ_\mathrm{p}\geq 0.68$). The spin in the direction of the angular momentum ($χ_\mathrm{eff}$) fluctuates more. Finally, again comparing to simulated signals, we show that any differences in the GW231123 inference based on each separate detector are not statistically significant. These results show that the properties of GW231123, and most importantly the high mass and high spin magnitudes inferred by NRSur7dq4, are robust.

gr-qc↗

arXiv2025

Measuring spin precession from massive black hole binaries with gravitational waves: insights from time-domain signal morphology

Robustly measuring binary black hole spins via gravitational waves is key to understanding these systems' astrophysical origins, but remains challenging -- especially for high-mass systems, whose signals are short and dominated by the merger. Nonetheless, events like GW190521 show that strong spin precession can indeed be gleaned. In this work, we track how spin precession imprints on simulated high-mass binary black hole signals cycle-by-cycle using time-domain inference. We investigate a suite of signals, all with the same spins and (near-unity) mass ratio -- yielding identical spin evolution -- but different signal-to-noise ratios (SNRs), total masses, and extrinsic angles, all of which affect the observed waveform morphology. We truncate each signal at various times and infer source parameters using only the data before or after each cutoff. The resultant posterior allows us to identify which time segments of each signal inform its spin precession constraints. We find that at a sufficiently high post-peak SNR ($\sim 20$), spin precession can be constrained by the NRSur7dq4 waveform model when just the post-peak data (i.e., ringdown) are visible. Similarly, at a large enough pre-cutoff SNR ($\sim 10$), spin precession can be constrained using only pre-peak data (i.e., inspiral); this occurs for signals with detector-frame total mass $\lesssim 100 M_{\odot}$ at GW190521's full-signal SNR. Finally, we vary the inclination, polarization, and phase angles, finding that their configuration need not be fine-tuned to measure spin precession, even for very high-mass and short signals with two to three observable cycles. We do not find that the same morphological features consistently drive precession constraints: in some signals, precession inference hinges on the relationship between a loud merger and quiet pre-merger cycle, as was the case for GW190521, but this is not generically true.

gr-qc↗

arXiv2025

Unified nonparametric equation-of-state inference from the neutron-star crust to perturbative-QCD densities

Perturbative quantum chromodynamics (pQCD), while valid only at densities exceeding those found in the cores of neutron stars, could provide constraints on the dense-matter equation of state (EOS). In this work, we examine the impact of pQCD information on the inference of the EOS using a nonparametric framework based on Gaussian processes (GPs). We examine the application of pQCD constraints through a "pQCD likelihood," and verify the findings of previous works; namely, a softening of the EOS at the central densities of the most massive neutron stars and a reduction in the maximum neutron-star mass. Although the pQCD likelihood can be easily integrated into existing EOS inference frameworks, this approach requires an arbitrary selection of the density at which the constraints are applied. The EOS behavior is also treated differently on either side of the chosen density. To mitigate these issues, we extend the EOS model to higher densities, thereby constructing a "unified" description of the EOS from the neutron-star crust to densities relevant for pQCD. In this approach the pQCD constraints effectively become part of the prior. Since the EOS is unconstrained by any calculation or data between the densities applicable to neutron stars and pQCD, we argue for maximum modeling flexibility in that regime. We compare the unified EOS with the traditional pQCD likelihood, and although we confirm the EOS softening, we do not see a reduction in the maximum neutron-star mass or any impact on macroscopic observables. Though residual model dependence cannot be ruled out, we find that pQCD suggests the speed of sound in the densest neutron-star cores has already started decreasing toward the asymptotic limit; we find that the speed of sound squared at the center of the most massive neutron star has an upper bound of $\sim 0.5$ at the $90\%$ level.

nucl-th↗

arXiv2025

Nonparametric extensions of nuclear equations of state: probing the breakdown scale of relativistic mean-field theory

Phenomenological calculations of the properties of dense matter, such as relativistic mean-field theories, represent a pathway to predicting the microscopic and macroscopic properties of neutron stars. However, such theories do not generically have well-controlled uncertainties and may break down within neutron stars. To faithfully represent the uncertainty in this breakdown scale, we develop a hybrid representation of the dense-matter equation of state, which assumes the form of a relativistic mean-field theory at low densities, while remaining agnostic to any nuclear theory at high densities. To achieve this, we use a nonparametric equation of state model to incorporate the correlations of the underlying relativistic mean-field theory equation of state at low pressures and transition to more flexible correlations above some chosen pressure scale. We perform astrophysical inference under various choices of the transition pressure between the theory-informed and theory-agnostic models. We further study whether the chosen relativistic mean-field theory breaks down above some particular pressure and find no such evidence. Using simulated data for future astrophysical observations at about two-to-three times the precision of current constraints, we show that our method can identify the breakdown pressure associated with a potential strong phase transition.

nucl-th↗

arXiv2025

PETRA: From the LISA global fit to a catalog of Galactic binaries

The Laser Interferometer Space Antenna (LISA) will detect mHz gravitational waves from many astrophysical sources, including millions of compact binaries in the Galaxy, thousands of which may be individually resolvable. The large number of signals overlapping in the LISA dataset requires a \emph{global fit} in which an unknown number of sources are modeled simultaneously. This introduces a \emph{label-switching ambiguity} for sources in the same class, making it challenging to distill a traditional astronomical catalog from global-fit posteriors. We present a method to construct a catalog by post-processing the global-fit posterior, relabeling samples to minimize the statistical divergence between the global fit and a factorized catalog representation. The resulting catalog consists of the source posterior distributions and their probabilities of having an astrophysical origin. We demonstrate our algorithm on two toy models and on a small simulated LISA dataset of Galactic binaries. Our method is implemented in the open-source Python package \textsc{petra\_catalogs}, and it can be applied in postprocessing to the output of any global-fit sampler.

gr-qc↗

arXiv2025

Glitches far from transient gravitational-wave events do not bias inference

Non-Gaussian noise in gravitational-wave detectors, known as "glitches," can bias the inferred parameters of transient signals when they occur nearby in time and frequency. These biases are addressed with a variety of methods that remove or otherwise mitigate the impact of the glitch. Given the computational cost and human effort required for glitch mitigation, we study the conditions under which it is strictly necessary. We consider simulated glitches and gravitational-wave signals in various configurations that probe their proximity both in time and in frequency. We determine that glitches located outside the time-frequency space spanned by the gravitational-wave model prior and with a signal-to-noise ratio, conservatively, below 50 do not impact estimation of the signal parameters.

gr-qc↗

arXiv2025

Mapping Parameter Correlations in Spinning Binary Black Hole Mergers

The spins of binary black holes measured with gravitational waves provide insights about the formation, evolution, and dynamics of these systems. However, interpreting these measurements-especially for heavy black holes-remains an open problem. While the imprint of spin during the inspiral phase, where the black holes are well-separated, is understood through analytic descriptions of the dynamics, no such expressions exist for the merger. Though numerical relativity simulations provide an exact solution (to within numerical error), the imprint of the full six spin degrees of freedom on the signal is not transparent. In the absence of analytic expressions for the merger and to advance our ability to interpret massive binary black hole spin measurements, here we propose a waveform-based approach. Leveraging a neural network to efficiently calculate mismatches between waveforms, we identify regions in the parameter space of spins and mass ratio that result in low mismatches and thus similar waveforms. We map these regions with a Gaussian fit, thus identifying correlations and quantifying their strength. For low-mass, inspiral-dominated systems, we recover the known physical imprint: larger aligned spins are correlated with more equal masses as they have opposite effects on the inspiral length. For high-mass, merger-dominated signals, a qualitatively similar correlation is present, though its shape is altered and strength decreases with larger total mass. Correlations between in-plane spins and mass ratio follow a similar trend, with their shape and strength altered as the mass increases. Our new methodology of waveform-based correlation mapping provides a first step toward systematically modeling spin effects in merger-dominated signals across the full intrinsic parameter-space and motivates future effective spin parameters beyond the reach of analytic methods.

gr-qc↗