arXiv subjects

Sophie Bini

Publications and source records attributed to Sophie Bini.

9 recordsLinked to original sources

arXiv2026

A compact time-frequency representation for gravitational-wave data analysis

Time-frequency (wavelet) domain analyses are seeing greater use for gravitational-wave data analysis due to the advantages they have in handling non-stationary noise. A popular choice is the Wilson-Daubechies-Meyer (WDM) wavelet transform, which uses a window function that is very compact in frequency, but more spread out in time. In this work, we consider an alternative window function that is built from a sum of phase-shifted Gaussians. This "Gaussian" window is more symmetric in time-frequency, and consequently more compact. We examine the properties of this window function and the implications it has for gravitational-wave analyses. We calculate the window's time-frequency variance product analytically and verify it numerically. For the symmetric case, where the window has the same form in time and frequency, the construction comes within $\sim2.4\%$ of saturating the Heisenberg-Gabor uncertainty limit. We conjecture that this Gaussian window achieves the minimum time-frequency area of any WD wavelet window.

gr-qc↗

arXiv2026

Eccentricity constraints disfavor single-single capture in nuclear star clusters as the origin of all LIGO-Virgo-KAGRA binary black holes

Multiple formation pathways have been proposed for the origin of binary black holes (BBHs). These include isolated binary evolution and dynamical assembly in dense stellar environments such as nuclear or globular star clusters. Yet, the fraction of BBHs originating from each channel remains uncertain. One way to constrain this fraction is by investigating the orbital eccentricities of the BH coalescences detected by the LIGO-Virgo-KAGRA (LVK) Collaboration. We analyze 84 BBHs from the first part of the fourth LVK observing run (O4a) using a multipolar, eccentric, aligned-spin effective-one-body waveform model. We perform parameter inference with neural posterior estimation and nested sampling. After incorporating astrophysical prior odds and comparing to the quasicircular precessing-spin hypothesis, we find that no candidates reach a high enough significance to claim a confident detection of eccentricity. We use these upper limits to explore a model, in which all O4a BBHs originate from single-single gravitational wave (GW) captures. We perform hierarchical inference on the velocity dispersion of the host environment of the BBHs and find $σ$ < 19.7 km/s (95% credible upper bound). This disfavors single-single capture in nuclear star clusters (approximately 20-200 km/s) as the dominant source of all observed BBH mergers. Our analysis also jointly infers the mass, spin and redshift distributions and takes into account selection effects due to using quasi-circular templates for BBH detection. Our results place improved constraints on the number of eccentric BBHs and highlight the importance of eccentricity measurements in disentangling compact-binary formation channels in current and future GW detectors.

astro-ph.HE↗

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

Optimizing searches for gravitational wave bursts using coherent WaveBurst 2G

The most general searches for gravitational wave transients (GWTs) rely on data analysis methods that do not assume prior knowledge of the signal waveform, direction, or arrival time on Earth. These searches provide data-driven signal reconstructions that are crucial both for testing available emission models and for discovering yet-to-be-uncovered sources. Here, we discuss progress in the detection performance of the coherent WaveBurst second-generation pipeline (cWB-2G), which is highly adaptable to both minimally modeled and model-informed searches for GWTs. Several search configurations for GWTs are examined using approximately 14.8 days of observation time from the third observing run by LIGO-Virgo-KAGRA (LVK). Recent enhancements include a ranking statistic fully based on multivariate classification with eXtreme Gradient Boosting, a thorough validation of the statistical significance accuracy of GWT candidates, and a measurement of the correlations of false alarms and simulated detections between different concurrent searches. For the first time, we provide a comprehensive comparison of cWB-2G performance on data from networks made of two and three detectors, and we demonstrate the advantage of combining concurrent searches for GWTs of generic morphology in a global observatory. This work offers essential insights for assessing our data analysis strategies in ongoing and future LVK searches for generic GWTs.

gr-qc↗

arXiv2024

Search for hyperbolic encounters of compact objects in the third LIGO-Virgo-KAGRA observing run

Gravitational-wave (GW) observations provide unique information about compact objects. As detectors sensitivity increases, new astrophysical sources of GW could emerge. Close hyperbolic encounters are one such source class: scattering of stellar mass compact objects is expected to manifest as GW burst signals in the frequency band of current detectors. We present the search for GW from hyperbolic encounters in the second half of the third Advanced LIGO-Virgo observing run (O3b). We perform a model-informed search with machine-learning enhanced Coherent WaveBurst algorithm. No significant event has been identified in addition to known detections of compact binary coalescences. We inject in the O3b data non-spinning third Post-Newtonian order accurate hyperbolic encounter model with component masses between [2, 100] $M_{\odot}$, impact parameter in [60, 100] ${GM}/{c^2}$ and eccentricity in [1.05, 1.6]. We further discuss the properties of the simulation recovered. For the first time, we report the sensitivity volume achieved for such sources, which for O3b data reaches up to 3.9$\pm 1.4 \times 10^5$ Mpc$^3$year for compact objects with masses between [20, 40] $M_{\odot}$, corresponding to a rate density upper limit of 0.589$\pm$0.094 $\times10^{-5}$Mpc$^{-3}$year$^{-1}$. Finally, we present projected sensitive volume for the next observing runs of current detectors, namely O4 and O5.

gr-qc↗

arXiv2023

An autoencoder neural network integrated into gravitational-wave burst searches to improve the rejection of noise transients

The gravitational-wave (GW) detector data are affected by short-lived instrumental or terrestrial transients, called glitches, which can simulate GW signals. Mitigation of glitches is particularly difficult for algorithms which target generic sources of short-duration GW transients (GWT), and do not rely on GW waveform models to distinguish astrophysical signals from noise, such as Coherent WaveBurst (cWB). This work is part of the long-term effort to mitigate transient noises in cWB, which led to the introduction of specific estimators, and a machine-learning based signal-noise classification algorithm. Here, we propose an autoencoder neural network, integrated into cWB, that learns transient noises morphologies from GW time-series. We test its performance on the glitch family known as blip. The resulting sensitivity to generic GWT and binary black hole mergers significantly improves when tested on LIGO detectors data from the last observation period (O3b). At false alarm rate of one event per 50 years the sensitivity volume increases up to 30% for signal morphologies similar to blip glitches. In perspective, this tool can adapt to classify different transient noise classes that may affect future observing runs, enhancing GWT searches.

gr-qc↗

arXiv2022

Search for gravitational-wave bursts in the third Advanced LIGO-Virgo run with coherent WaveBurst enhanced by Machine Learning

This paper presents a search for generic short-duration gravitational-wave (GW) transients (or GW bursts) in the data from the third observing run of Advanced LIGO and Advanced Virgo. We use coherent WaveBurst (cWB) pipeline enhanced with a decision-tree classification algorithm for more efficient separation of GW signals from noise transients. The machine-learning (ML) algorithm is trained on a representative set of noise events and a set of simulated stochastic signals that are not correlated with any known signal model. This training procedure preserves the model-independent nature of the search. We demonstrate that the ML-enhanced cWB pipeline can detect GW signals at a larger distance than previous model-independent searches. The sensitivity improvements are achieved across the broad spectrum of simulated signals, with the goal of testing the robustness of this model-agnostic search. At a false-alarm rate of one event per century, the detectable signal amplitudes are reduced up to almost an order of magnitude, most notably for the single-cycle signal morphologies. This ML-enhanced pipeline also improves the detection efficiency of compact binary mergers in a wide range of masses, from stellar mass to intermediate-mass black holes, both with circular and elliptical orbits. After excluding previously detected compact binaries, no new gravitational-wave signals are observed for the two-fold Hanford-Livingston and the three-fold Hanford-Livingston-Virgo detector networks. With the improved sensitivity of the all-sky search, we obtain the most stringent constraints on the isotropic emission of gravitational-wave energy from short-duration burst sources.

gr-qc↗

arXiv2022

Addressing the challenges of detecting time-overlapping compact binary coalescences

Standard detection and analysis techniques for transient gravitational waves make the assumption that detector data contains, at most, one signal at any time. As detectors improve in sensitivity, this assumption will no longer be valid. In this paper we examine how current search techniques for transient gravitational waves will behave under the presence of more than one signal. We perform searches on data sets containing time-overlapping compact binary coalescences. This includes a modelled, matched filter search (PyCBC), and an unmodelled coherent search, coherent WaveBurst (cWB). Both of these searches are used by the LIGO-Virgo-KAGRA collaboration. We find that both searches are capable of identifying both signals correctly when the signals are dissimilar in merger time, $|Δt_c| \geq 1$ second, with PyCBC losing only $1\%$ of signals for overlapping binary black hole mergers. Both pipelines can find signal pairings within the region $|Δt_c| < 1$ second. However, clustering routines in the pipelines will cause only one of the two signals to be recovered, as such the efficiency is reduced. Within this region, we find that cWB can identify both signals. We also find that matched filter searches can be modified to provide estimates of the correct parameters for each signal.

gr-qc↗