GDR Ondes Gravitationnelles : Rencontre du groupe de travail "méthodes d'analyse des données"

Liste des Contributions

6. Machine Learning for Gravitational Wave Data Analysis

Antsa Rasamoela (L2I Toulouse, CNRS/IN2P3, Université de Toulouse)

07/10/2026 09:00

Session 1

After a brief overview of Machine Learning fundamentals, this presentation offers a panorama of the main ML methods applied to gravitational-wave data processing: Simulation-Based Inference (SBI), transformer models, and diffusion models.

This panorama will be illustrated by the MANGO project, which is developing an ML framework to address the challenges posed by future detectors (Einstein...

11. Detection of GW stochastic background with Machine Learning

Hugo Einsle (Universiteit Antwerpen)

07/10/2026 09:45

Session 1

12. Search for supernovae with ML

Alberto Iess (LAPP CNRS)

07/10/2026 11:00

Session 2

2. Combining matched filtering with a convolutional neural network for CBC signal detection

Tito DAL CANTON (UPSaclay, CNRS/IN2P3, IJCLab)

07/10/2026 11:45

Session 2

When searching for compact binary mergers using matched filtering, we usually generate candidate events by finding the local maxima of the signal-to-noise ratio as a function of the search parameters, applying a threshold to them, and then forming a ranking statistic that combines the signal-to-noise ratio with signal-based discriminators, to account for nonstationary and non-Gaussian noise. I...

4. Accurate and efficient simulation-based inference for massive black-hole binaries with LISA

Alice Spadaro (L2I Toulouse, CNRS/IN2P3, Université de Toulouse)

07/10/2026 14:00

Session 3

We develop an accurate simulation-based inference framework for high-mass (≳ 10⁷M⊙) black-hole binaries observable by LISA. The method is implemented within the Dingo gravitational-wave parameter-estimation code, extending its application from ground-based detectors to the LISA band. We train a normalizing-flow model using aligned-spin higher-mode waveform models and a low-frequency...

8. A simulation-based inference pipeline for fast parameter estimation of massive black hole binaries with LISA data

Louis Le Saulnier (CEA/IRFU/DEDIP)

07/10/2026 14:20

Session 3

This work focuses on data analysis for LISA, with a particular focus on fast parameter inference of massive black hole binaries (MBHBs). Fast parameter estimation will be essential for LISA’s Low-Latency Alert Pipeline (LLAP), which will enable electromagnetic and multi-messenger follow-up of detected sources. This requires working with pre-merger signals, allowing other observatories to see...

9. Low-latency detection and parameter estimation of massive black hole binaries for LISA

Malvina Bellotti (Observatoire de la Côte d'Azur, Nice)

07/10/2026 14:40

Session 3

LISA will observe massive black hole binaries (MBHBs) hours to weeks before they merge. If these systems are in gas-rich environments, they might produce an electromagnetic (EM) counterpart: this motivates the development of pipelines able to localize and characterize the source fast enough to trigger a follow-up search. The main data analysis pipeline, the Global Fit, will model all LISA...

1. Diffusing the assumptions: realistic LISA stochastic component separation with physics-aware simulation-based inference

Iuliu Cuceu (Observatoire de la Côte d'Azur)

07/10/2026 15:00

Session 3

The numerous sources that LISA will observe pose a unique data analysis challenge, with the most promising current avenue being a Global Fit, a simultaneous inference of all sources. Within the LISA Global fit, stochastic sources, from instrumental noise, to the galactic foreground and astrophysical or cosmological backgrounds, are particularly relevant due to their effect on the quality of...

5. Plug-and-Play methods for reconstructing polarizations of gravitational wave signals

Thomas Sainrat (CentraleSupélec)

07/10/2026 15:20

Session 3

Reconstructing gravitational wave polarizations from detector data is a challenging problem due to the low SNR and the ill-posed nature of the the problem. We leverage the so-called "Plug-and-Play" methods, commonly used in inverse problem literature to achieve state-of-the-art image reconstruction. They explicitely separate the differentiable likelihood and the regularization, which is...

3. Fast localization of massive black hole binaries using the coronagraphic method for the LISA mission

Hugo Alexandre (LPC Caen)

07/10/2026 16:15

Session 4

The LISA constellation offers a unique opportunity to detect Massive Black Hole Binaries (MBHBs), with a sensitivity band that is particularly well suited to these sources. Their detection will allow us to trace the origins, growth and merger histories of massive black holes, to explore the fundamental nature of gravity and black holes and to probe the rate of expansion of the Universe using...

10. Earth's rotation impact on long-duration signals for 3G detectors

Mathieu Schoor (LAPP)

07/10/2026 16:35

Session 4

We studied the impact of the Earth's rotation on BNS signals that would be detected by Einstein Telescope (~ 2 hours).
Especially, we studied how the Match and the SNR would vary if we take (or not for simplicity) into account the modulations induced by the Earth's rotation on the templates

We show the results for the 2L and Xylophone configurations, both for a starting detection frequency...