Description du projet
Sonder les recoins sombres de l’univers grâce aux ondes gravitationnelles
Depuis la première observation directe des ondes gravitationnelles en 2015, les interféromètres LIGO et Virgo ont détecté plus de 50 ondes gravitationnelles générées par la fusion de systèmes stellaires binaires. Cependant, le temps de traitement par les ordinateurs des données relatives à ces fusions se compte en mois. Le projet Deledda, financé par l’UE, entend construire un nouveau modèle analytique permettant de comparer les données, afin de détecter des ondes gravitationnelles qui permettraient d’expliquer l’énergie noire. Ce modèle, qui exploite des techniques d’apprentissage automatique, espère proposer un nouveau moyen de détecter l’énergie et la matière noires qui constituent la majeure partie de notre Univers, encore jamais observées.
Objectif
Gravitational wave astronomy has opened an extraordinary new window to test the theory of gravity in the genuinely strong, highly dynamical and relativistic regime. The LIGO-Virgo Collaboration has now detected over 50 mergers of compact binary systems and this number will considerably increase in the coming years. There are currently two main issues related to the possibility of testing gravity with gravitational wave observations: the weakness of parametric tests of General Relativity to go beyond null tests and the very long inference time required by standard samplers which can take up to months. Specific waveform models and new techniques to speed up statistical inference are therefore crucial to maximise the scientific return of already available and upcoming data. In this project, we will construct an analytical model of the gravitational waves emitted during the late inspiral and merger of compact objects in theories of gravity that are cosmologically motivated, namely that have a chance to explain Dark Energy. We will then leverage deep learning techniques to promptly produce the posterior for the corresponding parameters given the detector data. To this aim, we will build up on two codes developed by one of the supervisors - ROMAN and PERCIVAL - which pioneered the use of machine learning in gravitational wave science. We will then apply this new pipeline to the real LIGO-Virgo data and perform Bayesian inference of Dark Energy parameters. All together this project will provide a new and complete framework to test the dark Universe with gravitational wave observations, exploiting state-of-the-art deep learning techniques.
Champ scientifique
- natural sciencesphysical sciencesrelativistic mechanics
- natural sciencesphysical sciencesastronomyobservational astronomygravitational waves
- natural sciencesmathematicsapplied mathematicsstatistics and probabilitybayesian statistics
- natural sciencesphysical sciencesastronomyastrophysicsdark matter
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Régime de financement
HORIZON-AG-UN - HORIZON Unit GrantCoordinateur
56126 Pisa
Italie