Descripción del proyecto
Exploración del universo oscuro mediante ondas gravitacionales
Desde la primera observación directa de las ondas gravitacionales en 2015, los interferómetros LIGO y Virgo han detectado más de cincuenta ondas gravitacionales procedentes de fusiones de sistemas estelares binarios. Sin embargo, los ordenadores tardan meses en procesar los datos de estas fusiones. El equipo del proyecto Deledda, financiado con fondos europeos, aspira a construir un nuevo modelo analítico con el que comparar los datos, que busca ondas gravitacionales capaces de ayudar a explicar la energía oscura. Mediante técnicas de aprendizaje automático, se espera que el modelo proporcione una nueva forma de detectar la energía y la materia oscuras no observadas que constituyen la mayor parte de nuestro universo.
Objetivo
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.
Ámbito científico
- 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
Programa(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Régimen de financiación
HORIZON-AG-UN - HORIZON Unit GrantCoordinador
56126 Pisa
Italia