arXiv subjects

Marie Anne Bizouard

Publications and source records attributed to Marie Anne Bizouard.

9 recordsLinked to original sources

arXiv2026

Can machine learning improve the detectability and disentanglement of the gravitational-wave background?

Gravitational waves from compact binary coalescences and from early Universe processes are expected to form a gravitational-wave background. We employ a custom deep learning multi-scale multi-headed autoencoder architecture to isolate gravitational-wave background from detector noise, followed by a Markov chain Monte Carlo inference stage to separate the astrophysical and cosmological components. Analyzing $108$-day mock datasets representative of the first period of the fourth LIGO-Virgo-KAGRA observing run, we show that we can detect with high confidence --- $\log_{10}$ noise Bayes factor larger than 3 --- a compact binary coalescence gravitational-wave background with an amplitude of $4.3^{+0.5}_{-0.4}\times10^{-9}$ at $f_{\rm ref}=25\,\mathrm{Hz}$, which is a factor $\sim5$ higher than the amplitude expected from compact binary sources. We also show that we can isolate a cosmological -- assumed flat spectrum -- gravitational-wave background as weak as $ 9.7^{+2.5}_{-2.4} \times 10^{-10}$ from the expected compact binary coalescence gravitational-wave background within simulated Gaussian noise mimicking the LIGO detectors sensitivity achieved in the fourth observing run. In blind-test comparisons with the standard \texttt{pygwb} pipeline, we show that our method achieves more accurate amplitude and spectral-index recovery and enables the separation of astrophysical and cosmological background components.

gr-qc↗

arXiv2026

Basilic: An end-to-end pipeline for Bayesian burst inference and model classification in gravitational-wave data

We present Basilic, a dedicated pipeline for Bayesian model selection and parameter estimation of short-duration gravitational-wave burst signals observable with ground-based detectors. Built on top of the bilby framework, Basilic combines modularity, pre-implemented burst models, and HTCondor integration to enable rapid, user-friendly analyses with minimal technical overhead. This work outlines the design philosophy, operational flow, and a set of example use cases demonstrating its scientific potential. As a case study, we also undertake an in-depth exploration of the comparison between a binary black hole merger and a cosmic string signal, through a parameter space exploration injection campaign. In addition to the well-known high-mass binary black-hole signal morphology degeneracy with cosmic string-like signals, we find that high anti-aligned component spins, even at moderate mass, can result in a similar degeneracy. Motivated by the likely low-SNR expected regime of possible future detections, we propose a data-driven study of model degeneracy, to be employed in the event of an inconclusive Bayes factor.

gr-qc↗

arXiv2025

Application of Non-Linear Noise Regression in the Virgo Detector

This work presents the first demonstration of non-linear noise regression in the Virgo detector using deep learning techniques. We use DeepClean, a convolutional autoencoder previously shown to be effective in denoising LIGO data, as our tool for modeling and subtracting environmental and technical noise in Virgo. The method uses auxiliary witness channels to learn correlated noise features and remove them from the strain data. For this study, we apply DeepClean to Virgo O3b data, using 225 witness channels selected across 13 targeted frequency bands. Our analysis confirms the presence of non-linear couplings in the subtracted noise, highlighting the importance of DeepClean-like tools in capturing such effects. We observe up to a 1.3 Mpc improvement in the binary neutron star inspiral range (~2.5% gain), and an average increase of 1.7% in the recovered signal-to-noise ratio for injected binary black hole signals. Parameter estimation studies further confirm that DeepClean does not introduce bias in the recovery of source parameters. These results demonstrate the robustness of DeepClean on Virgo data and support its adoption in real-time noise subtraction frameworks for future observing runs.

gr-qc↗

arXiv2025

The Science of the Einstein Telescope

Einstein Telescope (ET) is the European project for a gravitational-wave (GW) observatory of third-generation. In this paper we present a comprehensive discussion of its science objectives, providing state-of-the-art predictions for the capabilities of ET in both geometries currently under consideration, a single-site triangular configuration or two L-shaped detectors. We discuss the impact that ET will have on domains as broad and diverse as fundamental physics, cosmology, early Universe, astrophysics of compact objects, physics of matter in extreme conditions, and dynamics of stellar collapse. We discuss how the study of extreme astrophysical events will be enhanced by multi-messenger observations. We highlight the ET synergies with ground-based and space-borne GW observatories, including multi-band investigations of the same sources, improved parameter estimation, and complementary information on astrophysical or cosmological mechanisms obtained combining observations from different frequency bands. We present advancements in waveform modeling dedicated to third-generation observatories, along with open tools developed within the ET Collaboration for assessing the scientific potentials of different detector configurations. We finally discuss the data analysis challenges posed by third-generation observatories, which will enable access to large populations of sources and provide unprecedented precision.

gr-qc↗

arXiv2025

GW231123: Binary Black Hole Merger or Cosmic String?

The LIGO-Virgo-KAGRA Collaboration recently reported an exceptional gravitational-wave event, GW231123. This gravitational-wave signal was assumed to be generated from the merger of a binary black hole system, with source frame masses of $137^{+22}_{-17}~\textup{M}_\odot$ and $103^{+20}_{-52}~ \textup{M}_\odot$ (90\% credible intervals). As seen by the two LIGO detectors, the signal has only $\sim 5$ cycles, between 30 and 80 Hz, over $\sim 10$ ms. It is of critical importance to confirm the origin of this signal. Here we present the results of a Bayesian model comparison to test whether the gravitational-wave signal was actually generated by a binary black hole merger, or emitted from cusps or kinks on a cosmic string. We find significant evidence for a binary black hole merger origin of the signal.

gr-qc↗

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.

gr-qc↗

arXiv2023

Understanding the progenitor formation galaxies of merging binary black holes

With nearly a hundred gravitational wave detections, the origin of black hole mergers has become a key question. Here, we focus on understanding the typical galactic environment in which binary black hole mergers arise. To this end, we synthesize progenitors of binary black hole mergers as a function of the redshift of progenitor formation, present-day formation galaxy mass, and progenitor stellar metallicity for $240$ star formation and binary evolution models. We provide guidelines to infer the formation galaxy properties and time of formation, highlighting the interplay between the star formation rate and the efficiency of forming merging binary black holes from binary stars, both of which strongly depend on metallicity. We find that across models, over 50% of BBH mergers have a progenitor metallicity of a few tenths of Solar metallicity, however, inferring formation galaxy properties strongly depends on both the binary evolution model and global metallicity evolution. The numerous, low-mass black holes ($\mathrm{\lesssim 15\,M_{\odot}}$) trace the bulk of the star formation in galaxies heavier than the Milky Way ($M_\mathrm{Gal}$ $\mathrm{\gtrsim 10^{10.5}\,M_{\odot}}$). In contrast, heavier BBH mergers typically stem from larger black holes forming in lower metallicity dwarf galaxies ($M_\mathrm{Gal}$ $\mathrm{\lesssim 10^{9}\,M_{\odot}}$). We find that the progenitors of detectable binary black holes tend to arise from dwarf galaxies at a lower formation redshift ($\lesssim \, 1$). We also produce a posterior probability of the progenitor environment for any detected gravitational wave signal. For the massive GW150914 merger, we show that it likely came from a very low metallicity ($Z$ $\mathrm{\lesssim}\,0.025\,\mathrm{Z_{\odot}}$) environment.

astro-ph.HE↗

arXiv2020

Science Case for the Einstein Telescope

The Einstein Telescope (ET), a proposed European ground-based gravitational-wave detector of third-generation, is an evolution of second-generation detectors such as Advanced LIGO, Advanced Virgo, and KAGRA which could be operating in the mid 2030s. ET will explore the universe with gravitational waves up to cosmological distances. We discuss its main scientific objectives and its potential for discoveries in astrophysics, cosmology and fundamental physics.

astro-ph.CO↗

arXiv2013

Searching for gravitational waves with the LIGO and Virgo interferometers

The first generation of ground-based interferometric gravitational wave detectors, LIGO, GEO and Virgo, have operated and taken data at their design sensitivities over the last few years. The data has been examined for the presence of gravitational wave signals. Presented here is a comprehensive review of the most significant results. The network of detectors is currently being upgraded and extended, providing a large likelihood for observations. These future prospects will also be discussed.

gr-qc↗