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Deep Learning the Dark Universe with Gravitational Waves

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... ver más

31/08/2026

UNIPI

265K€

Presupuesto del proyecto: 265K€

Líder del proyecto

UNIVERSITA DI PISA No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 3 Participantes

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-05-18

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

HORIZON-MSCA-2021-PF-01-01: MSCA Postdoctoral Fellowships 2021

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Cerrada hace 3 años

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3 Participantes

UNIPI

265.10K€ | Lider

CALIFORNIA INSTITUTE OF TECH...

N.D. | Participante

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Últimas noticias

25-11-2024: ECE En las últimas 48 horas el Organismo ECE ha otorgado 52 concesiones

25-11-2024: CMVAMADRID En las últimas 48 horas el Organismo CMVAMADRID ha otorgado 1 concesiones

25-11-2024: FUNDACION-EOI En las últimas 48 horas el Organismo FUNDACION EOI ha otorgado 6 concesiones

Duración del proyecto: 51 meses Fecha Inicio: 2022-05-18
Fecha Fin: 2026-08-31

Líder del proyecto

UNIVERSITA DI PISA

TRL 4-5

Presupuesto del proyecto 265K€

Descripción del proyecto 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.

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