We will give an overview of the current projects using machine learning in the context of gravitational wave astronomy, the results obtained so far and their potential impact.
A major activity of the LIGO-Virgo collaboration is to build algorithms able to infer from the detected gravitational wave signals the posterior distributions of the parameters defining their sources: angles in the sky, distance from us, masses etc. Current algorithms like MCMC and Nested Sampling have already demonstrated with success their ability to do so during the first and second run of...
Gravitational waves are polarized. Their polarization is essential to characterize the physical and dynamical properties of the source i.e., a coalescing binary of two compact objects such as black holes or neutron stars. Observations with two or more non coaligned detectors like Virgo and LIGO allow to reconstruct the two polarization components usually denoted by h+(t) and h(t). The...
This talk will review the key issues that has to be addressed to extract the full science potential from the data of the LISA space mission. We will describe the LISA data challenges which will provide the main framework to develop and test data analysis algorithms.
Cet exposé présentera une analyse temps fréquence des ondes gravitationnelles en utilisant une nouvelle méthode: le synchrosqueezing d’ordre supérieur fondé sur la transformée de Fourier à court terme (TFCT). Tout d’abord, nous rappellerons le modèle mathématique de signal multicomposantes (SMCs) defini comme une superposition de modes modulés à la fois en amplitude et en fréquence (appelés...
